Software Platforms for Electronic/Atomistic/Mesoscopic Modeling: Status … · · 2018-05-15Software Platforms for Electronic/Atomistic/Mesoscopic Modeling: ... Inthispowerfulandelegantmethod,abinitio

THEMATIC SECTION: 2ND INTERNATIONALWORKSHOP ON SOFTWARE SOLUTIONS FOR ICME

Software Platforms for Electronic/Atomistic/MesoscopicModeling: Status and Perspectives

Mikael Christensen1& Volker Eyert1 & Arthur France-Lanord1

& Clive Freeman2&

Benoît Leblanc1 & Alexander Mavromaras1 & Stephen J Mumby3 & David Reith1&

David Rigby3 & Xavier Rozanska1 & Hannes Schweiger3 & Tzu-Ray Shan3&

Philippe Ungerer1 & René Windiks1 & Walter Wolf1 & Marianna Yiannourakou1&

Erich Wimmer1,3

Received: 16 November 2016 /Accepted: 8 December 2016 /Published online: 13 March 2017# The Minerals, Metals & Materials Society 2017

Abstract Predicting engineering properties of materialsprior to their synthesis enables the integration of theirdesign into the overall engineering process. In this con-text, the present article discusses the foundation and re-quirements of software platforms for predicting materialsproperties through modeling and simulation at the elec-tronic, atomistic, and mesoscopic levels, addressing func-tionality, verification, validation, robustness, ease of use,interoperability, support, and related criteria. Based onthese requirements, an assessment is made of the currentstate revealing two critical points in the large-scale indus-trial deployment of atomistic modeling, namely (i) theability to describe multicomponent systems and to com-pute their structural and functional properties with suffi-cient accuracy and (ii) the expertise needed for translat-ing complex engineering problems into viable modelingstrategies and deriving results of direct value for the en-gineering process. Progress with these challenges is un-deniable, as illustrated here by examples from structuraland functional materials including metal alloys, poly-mers, battery materials, and fluids. Perspectives on theevolution of modeling software platforms show the needfor fundamental research to improve the predictive powerof models as well as coordination and support actions toaccelerate industrial deployment.

Keywords Integrated computational materials engineering(ICME) .Materials modeling . Software . Interoperability .

Industrial deployment . Metal alloys . Polymers . Batteries .

Fluids

Introduction

We are witnessing the dawn of a Golden Age of integratedcomputational materials engineering (ICME). The confluenceof five main factors is creating this unprecedented situation,namely (i) theoretical physics and chemistry have establisheda solid scientific foundation in the form of classical mechan-ics, electrodynamics, statistical thermodynamics, and quan-tum mechanics; (ii) computer hardware with astounding per-formance has become readily affordable; (iii) advanced soft-ware systems are enabling unprecedented productivity whilethe tools for software development are constantly improving;(iv) today’s communication technologies enable instantaneousand global collaboration as well as access to a daunting wealthof data; and, last but not the least, (v) the potential economicimpact of this technology has aroused the interest of industryaround the globe, thus driving the accelerated transition fromacademic research to practical applications.

The vision of ICME is illustrated in Fig. 1. The design ofmaterials is treated as an integral part of the overall engineer-ing process. Rather than being restricted to existing materialsin the design of components and systems, the most fundamen-tal building blocks of any engineering endeavor, namely thematerials themselves, become dynamic variables in the designprocess. To this end, the ability to compute properties of ma-terials prior to their actual synthesis is a key requirement forICME. This capability is the foundation of ICME and, right-fully, this has created tremendous excitement and

* Erich [email protected]

1 Materials Design s.a.r.l., Montrouge, France2 Materials Design, Inc., Angel Fire, NM, USA3 Materials Design, Inc., San Diego, CA, USA

Integr Mater Manuf Innov (2017) 6:92–110DOI 10.1007/s40192-017-0087-2

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opportunities. Thus, this paper focuses on the ability to com-pute and use materials property data.

There are also important modeling applications in the fieldof raw natural materials. These materials can be either min-erals (e.g., rocks, ores, clay) or organic (e.g., wood, coal, ker-ogen, asphalt). They are used or processed by important in-dustrial sectors including oil and gas, building materials, met-allurgy, energy, and chemistry. Understanding the propertiesof these materials is required for the design of safe and eco-nomic processes in these branches. Due to their complexity,they are particularly challenging for modeling, but substantialprogress is already being made.

At present, simulations based on classical mechanics, fluiddynamics, and electrodynamics are well established R&Dtools in the design of components and systems, leading tobetter products, reduced development time, and lower exper-imental costs. The benefits are evident such as safer and bettercars, highly efficient airplanes and turbines, and a myriad ofelectronic products. However, the materials property data re-quired for these macroscopic simulations are for the most parttaken from experiment. Hence, the introduction of new mate-rials hinges on experimental synthesis and characterization,which are often slow and expensive bottlenecks.

While experiments will always be needed, the capability ofcomputing properties of materials prior to their synthesis rad-ically changes this picture. This is a wonderful situation, butthe reality of predicting the properties of a material at a level ofaccuracy and reliability to be useful in an engineering processis challenging. Successes are emerging, but it is fair to say thatthe technological readiness level of electronic and atomisticsoftware, which is a fundamental part of ICME, leaves ampleopportunities for improvement.

It is the purpose of the present paper to review the currentstate of software platforms, which enable materials propertypredictions based on a combination of electronic structuremethods, atomistic simulation tools using interatomic potentialsor forcefields, andmesoscopicmodeling tools. In the following,this combination will be referred to as “e/a/m.” The focus hereis on approaches based on fundamental physical concepts suchas Schrödinger’s equation and statistical thermodynamics. One

needs to keep in mind that in practice, empirical methods suchas quantitative structure-property relationships (QSPR), datamining, and machine learning play an important role. In fact,these approaches can be very effective if used in combinationwith physical equation-based predictive methods.

Theoretical Foundation

Computational materials science has its theoretical foundationin quantum mechanics, statistical mechanics, classical me-chanics, and electrodynamics. Within the approaches basedon physical laws, one distinguishes between “discrete” and“continuum” models, the former referring to methods withexplicit treatment of electrons, atoms, or groups of atoms,the latter encompassing a vast range of methods using a con-tinuum description of matter. Structural analysis with finiteelement methods (FEM), computational fluid dynamics(CFD), and the so-called technology computer-aided design(TCAD) methods for the simulation of electronic circuits be-longs to this very important and well-established class ofmethods. In materials science, thermodynamic approachessuch as the calculation of phase diagrams (CALPHAD), phasefield methods, and the solution of diffusion equations for sim-ulating, for example, solidification of metal alloys, play anincreasingly important role. At present, the established contin-uum methods rely overwhelmingly on experimentally deter-mined materials property data, although increasingly, thesemethods also incorporate data from atomistic and quantummechanical simulations, as indicated in Fig. 2.

Historically, the methods shown in Fig. 2 have been devel-oped by different groups, initially mostly in academic researchgroups, government laboratories, and in some cases by indus-trial research organizations such as Bell Labs and the researchcenters of IBM in Yorktown Heights and Rüschlikon. Thework by different research groups in physics, chemistry, ma-terials science, and biology, and the different focus, assump-tions, and approximations used in describing various materialsand properties have resulted in a fragmentation of this field,which persists to the present day.

On the level of ab initio quantum mechanical approaches,theoretical chemists and solid-state physicists pursued differ-ent routes for many decades. Many quantum chemists aimedat the most accurate solution of Schrödinger’s equation forsmall molecules starting from Hartree-Fock theory while the-oretical solid-state physicists developed methods based ondensity functional theory (DFT) [1, 2]. During the past de-cades, DFT has become a workhorse also for molecular sys-tems while Hartree-Fock-based methods have found their wayinto solid-state methods. Driven by the quest for higher accu-racy and enabled by increasing compute power, we arewitnessing today a convergence of these two approaches, forexample in the form of hybrid functionals.

Fig. 1 Scheme of integrated computational materials engineering (Colorfigure online)

Integr Mater Manuf Innov (2017) 6:92–110 93

Semi-empirical quantum mechanical approaches as imple-mented in programs such as MOPAC [3] are extremely usefuldue to their high computational efficiency and good perfor-mance especially for organic and inorganic molecular sys-tems. The use of extensive training sets of experimental datain the parameterization while maintaining key aspects of mo-lecular quantum mechanics are the foundation for the successof this approach. Semi-empirical calculations can be 100 timesfaster than ab initio calculations, thus offering an interestingapproach for high-throughput calculations [4]. Furthermore,semi-empirical methods can be implemented such that thescaling of the computing time is better than N2 while ab initioapproaches typically scale with a power of 3 or higher, with Nbeing the number of electrons, although the so-called order-Nab initio methods exist as well.

The modeling and property predictions of liquids [5] andamorphous materials such as polymers and glasses, as well assimulations of dislocations in metal alloys, may require thesampling of millions of configurations or large systems con-taining hundreds of thousands of atoms or more. The desire tomodel such systems has led to the development of forcefield(or force field) methods, a terminology preferably used bychemists [6, 7, 8] or interatomic potentials, preferably usedby physicists [9, 10]. Both terms refer to the same concept,namely, the use of relatively simple mathematical expressionssuch as Morse-type binding curves to describe the interactionbetween atoms. A major incentive for the development offorcefields was the desire to simulate the interaction of drugmolecules with DNA and proteins. Extension of theseforcefields to synthetic polymers and organic liquids has en-abled the prediction of structural, thermomechanical, and rhe-ological properties with remarkable accuracy. Interatomic

potentials have also been successful in describing metallicsystems as well as highly ionic materials. However, theseapproaches are different in character and thus mixed systemssuch as an interface between a polymer and a metal represent aconceptual dilemma. In this context, it should be pointed outthat the Nobel prize for chemistry was awarded to MartinKarplus, Michael Levitt, and Arieh Warshel in 2013 for the“Development of multi-scale models for complex chemicalsystems.”

The optimization of forcefield parameters using resultsfrom ab initio calculations as training set is one method toexpand the scope of ab initio methods. Calibrating theforcefield parameters on sensitive quantities such as the ex-perimental density, heat of formation, mechanical properties,or the melting point of a material leads to powerful computa-tional approaches, which can be more accurate than DFT cal-culations, albeit with a narrower range of applicability, as willbe illustrated by the prediction of boiling points of liquidsdiscussed in a later section.

The so-called cluster expansion method [11, 12] offers an-other possibility to expand the scope of ab initio calculations.In this powerful and elegant method, ab initio calculations areused iteratively to build an expression of the total energy of asystem as a function of local arrangements (clusters) of atoms.This expansion reproduces the original DFT values with afidelity of a few milli-electron-volts (or a few tenths of akJ mol−1). However, the presence of a lattice is required.Hence, this method is particularly well suited for the simula-tion of metal alloys.

Kinetic Monte Carlo simulations can describe phenomenaon very long time scales and on large systems containingmillions of atoms. For example, the dynamic evolution ofphase segregation in a metal alloy can be simulated, if thejump rates of elementary diffusion steps are known.Diffusion rates and, more generally, reaction rates can be ob-tained from ab initio calculations using transition state theoryor specific forms of molecular dynamics.

Coarse-graining is yet another possibility within the class offorcefield methods to extend the length and time scales of atom-istic simulations. This method can be applied to performmolec-ular dynamics, Monte Carlo simulations, Brownian dynamics,anddissipativeparticledynamics.Whileconceptuallyappealing,the construction of accurate coarse-grainedmodels requires sub-stantial insight into the key interactionmechanisms of amaterialand thus is far from being an automatic process.

This short overview of e/a/m methods gives a glimpse onthe contrasting simplifications of the various approaches andthe resulting difficulties in integrating these approaches into aunified modeling platform with smooth interoperabilityamong the different approaches as well as connecting such aplatform with simulations operating on the continuum level.

Toassess thepresentmodelingplatforms,wewillnowdiscussthe requirements for such a software system. While great

Fig. 2 Computational methods in materials modeling. MD moleculardynamics, MC Monte Carlo, BD Brownian dynamics, DPD dissipativeparticle dynamics, FEM finite element methods, CFD computationalfluid dynamics, CALPHAD calculation of phase diagrams, TCADtechnology computer-aided design, QSPR quantitative structure-property relationship (Color figure online)

94 Integr Mater Manuf Innov (2017) 6:92–110

progress has been achieved during the past decades, it will alsobecome obvious that in many instances, these requirements areonly partially fulfilled, thus leaving room for majorimprovements.

Status of e/a/m Modeling Platforms

Material modeling platforms serve two closely connected pur-poses, namely (i) enabling a deeper understanding of mecha-nisms that lead to a certain behavior of materials, for examplestress corrosion cracking, and (ii) predicting properties of ma-terials prior to their synthesis and experimental characteriza-tion, for example solid-state electrolytes for Li-ion batteries.Here, we assess the status of e/a/m modeling platforms guidedby their key attributes. To this end, we draw on experience inthe development of the modeling and simulation platformsInsight II and Discover (Biosym Technologies), Cerius2(Molecular Simulations, Inc.), Materials Studio (Accelrys),UniChem (Cray Research), and MedeA (Materials Design,Inc.). It should be noted that this selection is based on thedirect experience of the authors and is not intended to beexhaustive. A comprehensive compilation of such platformand simulation tools is given by Schmitz and Prahl [13].

A modern e/a/m software platform should meet the followingcriteria:

& Comprehensive—predictionsofall relevantphysicalandchem-icalproperties forall typesofmaterialssuchasmetalsandalloys,semiconductors,andinsulators; inorganicandorganicmaterials;crystalline and amorphous phases such as glasses and polymersaswell as fluids in liquid and gaseous form.

& State-of-the-art—computational materials science is anactive research field with new methods emerging at a re-lentless pace. Users of leading platforms expect the bestand most recent methods to be available.

& Verified—tests need to show that algorithms are correctlyimplemented and that programming bugs have been foundand corrected. Verification of computer codes need to berepeated after changes are made to the software or whenhardware or operating systems change.

& Validated—computed properties need to be validatedagainst experimental data with estimation of error bars.

& Robust—in the coming age of high-throughput calcula-tions, all simulation programs need to work correctly un-der a large range of input conditions.

& Error recovery and fault tolerance—errors in the compu-tations, whatever their origin might be, must be automat-ically detected and, if possible, corrected.

& Ease of use—time to solution is critical and needs to beminimized; input parameters should be kept to a minimumwithout undue complexity nor redundancy.

& Standardized—computer programs should use standard-ized procedures to facilitate the exchange of data fromsimulations performed by different people at differenttimes with different programs.

& Ability to create, store, and re-use workflow protocols—providing traceability and reusability are essential require-ments for industrial use and quality control.

& Well documented—context-dependent documentation ofthe underlying theory, algorithms, and tutorials are impor-tant parts of any good software system.

& Computationally efficient—the performance of hardwarecontinues to progress and should be fully utilized by anysoftware platform.

& Supported consistently on evolving hardware and operat-ing systems—acquiring the skills to use any software plat-forms for materials simulations to its fullest capabilitiesrepresents an investment of years. This investment needsto be protected by support over many years.

& Extensible— theory, algori thms, and softwareimplementations evolve and any sustainable software plat-form needs to be able to incorporate new capabilities with-out having to rewrite entire codes.

& Portable—scientific software has a longer life cycle thancomputer hardware and operating systems; softwareshould be written in a form which facilitates portabilityto new hardware and operating systems.

& Interoperable with other software components and plat-forms—modeling and simulations of materials involvetools from different sources and suppliers; standardizationand interoperability are mandatory.

& Able to communicate efficiently with public and proprie-tary databases—modeling and simulations of materials arebuilt on previous knowledge and data; a close integrationof existing experimental data with e/a/m simulation plat-forms sets the stage for powerful materials property min-ing and optimization.

& Tracking of metadata for traceability and re-use—like ex-perimental data, computed results require metadata tomake them useful such as input data, computational pa-rameters, date and time, links to input and output files,program version, operating system, time, and operator.

Based on these requirements, the current status is discussedin detail in the following sections.

Comprehensive Functionality

Industrial materials and processes often involve multiplephases, for example a liquid and solid phase in an additivemanufacturing process, a carbide precipitate in steel, apolymer/ceramic interface in an electronic package, or awater/oxide interface in a corrosion problem. Thus, an e/a/mplatform needs to be able to handle a broad range of materials


including metals and alloys, ceramics, and semiconductors, aswell as organic materials in crystalline, amorphous, liquid, andgaseous forms. For each of these materials as well as for theinterfaces between any combination of such materials, theplatform needs to be able to permit the computation of a rangeof materials properties as illustrated in Fig. 3.

Current e/a/mplatformsaddress awide rangeofmaterials andproperties, as shown in Fig. 3, but the technological readinesslevel of such property calculations is not uniform. For example,elastic coefficients of crystalline solids and synthetic polymerslike cross-linked epoxy resins can be computedwith remarkableaccuracy and predictive power while the accurate prediction ofother properties such as the melting temperature of even somecommon crystalline materials remains as amajor challenge.

Historically, the modeling of different classes of materialshas evolved from research efforts of quite different scientificcommunities. This has led to a rangeofdifferent theoretical andcomputational approaches, terminologies, and different units.Thus, there are nowmodeling methods which address specificmaterials and properties quite well, but are applicable only toindividual systems such as a bulkmetal or an organic polymer,but not to an interface between a polymer and a metal.

Fortunately, as computational approachesmature, they tendto become more general, thus allowing broader modeling ofcomplex multi-phase systems. For example, electronic struc-turemethods for solids on the one hand and quantumchemistrymethods for molecules on the other hand have been developedfor well over half a century by different research groups. Thesolid-state communitypursueddensity functional theory (usingRydberg atomic units or eV) while quantum chemists built onHartree-Fock methods (using Hartree atomic units orkcal mol−1). To further complicate matters, the definition of“mol” could mean mol of atoms, mol of compounds, or molof simulation boxes. Today, these methods have merged andone can readily compute the interaction of a molecule with ametal surface. Nevertheless, domain-specific computationalmethods are still predominant presenting a challenge for thepractitioner who wants to solve multi-phase and multi-materials problems in a single modeling platform.

Today’s leading e/a/m modeling platforms provide in asingle environment access to a comprehensive range ofmethods including electronic structure methods for solids, sur-faces, and molecules as well as forcefield-based methods.These programs are accessible from a single user interface,thus allowing the choice of the most appropriate method forthe question or materials property at hand. In addition, thereare specialized platforms with a focus on specific properties,for example thermo-physical properties of molecular liquids.

State of the Art

Innovative methods in computational materials science arebeing developed by an increasing number of research groups

around the world. Users of advanced simulation platformsexpect to benefit from the best of these developments withoutundue delay. These could involve, for example, new func-tionals in DFT methods, new algorithms for highly accuratepost-DFT approaches such as the random phase approxima-tion in electronic structure methods [14], or it could be a newforcefield or a new statistical ensemble. Of course, rapid ac-cess to new capabilities competes with other requirementssuch as validation, robustness, and standardization.

Development andmarketing strategies for materials model-ing software vary. If the software is intended for a broader userbase, then packagingmay take precedence over rapid access tothe latest and greatest computational methods. Academicsources of software tend to put a high priority on state-of-the-art functionality whereas some commercial software platformsseek a broader user base by providing ease-of-use and automa-tion. Each strategy has its merits and drawbacks and the end-users select the appropriate environment given their needs.

Verification

Both scientific and commercial applications of software relyon the correct implementation of algorithms and the absenceof programming errors. Verification is a very difficult andtime-consuming part of software engineering. Static verifica-tion is usually the first step, where the source code is beinginspected for aspects such as compliance with the standards ofa chosen computer language and the absence of simple typo-graphical errors. Dynamic verification involves the executionof tests where the answer is known from other sources.

When new features are implemented in a software, regres-sion tests are used to verify that existing functionality is pre-served. This test is also critical when the software is ported, forexample, to new operating systems, but also when new com-pilersor libraries areused.Non-regression tests arenecessary to

Fig. 3 Properties for materials and their interfaces, which an e/a/mplatform needs to predict. The two axes at the base indicate possibleinterfaces between different materials (Color figure online)


ensure that improved or new features indeed have the desiredeffect. Both types of tests are necessary. It is good practice thattesting isperformedbyothers than theoriginalcodedevelopers.

If a new computational method together with results forspecific cases is already published, then a new implementationof such a method should be tested against these earlier results.A recent example is a comparison of DFT results obtainedwith three different computer codes, namely VASP, GPAW,and Wien2k [15]. This study demonstrated that the variationbetween the results from these three different implementationsvary by an order less than the typical deviation of the comput-ed results from experiment. This example also indicates thatverification and validation often go hand in hand.

Validation and Estimation of Error Bars

Practical solutionsofmany industrialproblemsdonot require themost accurate approaches. However, frequently one needs fastand cost-effective property values with sufficient accuracy andreliability to be used for making engineering decisions. This re-quirement leads to theneed for reliableerror estimates, especiallyfor approximate methods. A priori such estimates are very diffi-cult to make. Hence, the assessment by experts are necessary inthis case, possibly aided by statistical methods.

Validation of computational approaches and the analysis ofuncertainty thus remains a central requirement of e/a/m plat-forms. Systematic comparison with accurate and reliable ex-perimental data provides the most compelling analysis.However, one should keep in mind that occasionally experi-mental data are incorrect, for example simply due to typo-graphical transcription errors or due to uncontrolled effectsduring the measurements. In fact, high-level computationscan play a critical role in identifying such errors and deficien-cies. Illustrative examples are provided by the realization thatthe reported elastic constants of sapphire had for many yearsincluded an indexing error [16] and an incorrect feature of theband structure was reported for InAs, a standard III-V semi-conductor [17]. In both these instances, the source of the ex-perimental discrepancy was identified based on accurate com-putation. Industrial research and development experience,which frequently does not immediately emerge in the researchliterature, indicates that these are far from isolated examples.

Robustness

In the late twentieth century, when compute power was muchmore limited and expensive than today, typical modeling effortsentailed in the study of individual or a handful of systems. In thissituation, it was quite feasible tomonitor the progress of calcula-tions and to adjust computational parameters suchasbasis sets ofquantum mechanical calculations or convergence criteria in ge-ometryoptimizationsbyhand.This isno longerpractical inhigh-

throughput calculations on thousands of systems, which are en-abled by today’s compute power.

For this reason, robustness of “compute engines” is becom-ing a critical aspect of modern simulation platforms. Thismeans that the right choices of computational parameterscan no longer be made by hand for each system, but they needto be universal over a large set of systems. Before launchingany simulations, the modeling platform needs to assess theformal correctness and consistency of input structures andcomputational parameters. This includes simple aspects suchas avoiding that input structures contain atoms which areunphysically close, but it also can mean that a model ofcross-linked polymer with 100,000 atoms does not containunphysical topologies such as highly strained ring catenation.Leadingmodeling platforms provide such consistency checks.

Today’s leading quantum mechanical solid-state programssuch as VASP are remarkably general in terms of the choice ofatoms (from hydrogen to curium) and robust in terms of initialstructures in geometry optimizations or ab initio molecular dy-namic simulations. The situation is quite different in forcefield-basedmoleculardynamicsandMonteCarlosimulations.Whileexcellent forcefields (in terms of generality and accuracy) formanyorganicmolecules are available,which today’smodelingplatforms can assign automatically in a large variety of modelssuch asmolecular liquids and polymers, occasionally function-al groups ormultifunctionalmolecules are encountered, wheresome forcefield parameters aremissing. This issue ismore pro-nouncedwhenforcefields(or interatomicpotentials)areneededto describe inorganic systems and/or systems comprising inor-ganic and organic matter. For many cases, classical forcefieldssimply do not exist or transferability of parameters cannot beachieved.Usingsuchforcefields inablind fashioncan thenleadto unexpected results or perhaps unmitigated disasters. The factthat a given forcefield is not applicable to a specific systemor toa certain property may not be obvious even to the experiencedusers. No current modeling platform can guarantee robustnessin every situation. Rather, the platform should warn the userbefore launching a simulation, in cases where the initial modelof the system(s) or the description of the interatomic interac-tions may be unphysical.

Another aspect of robustness is the behavior of the modelingplatform when the size of the system or the required simulationtime exceeds the available computing resources.While it wouldbe desirable that the user is informed about the necessary com-puting resources prior to launching a simulation, estimating suchresource requirements is demanding and today’s modeling plat-forms usually do not currently offer such a feature.

Robustness of a software system also implies stable behav-ior under any operating conditions. State-of-the-art modelingplatforms involve very complex human/machine interactionsas well as asynchronous, non-deterministic inter-process com-munications, for example between a user interface and tasksrunning on different computers in a network. Sophisticated


software engineering, employing established communicationpatterns and protocols, make such systems reliable.

Error Detection and Correction, Fault Tolerance

Errors can occur in any modeling step including the buildingof structural models, the setting of input parameters, lack ofconvergence in iterative procedures, and numerical instabil-ities, as well as in the analysis, storage, and retrieval of com-puted results. Furthermore, today’s highly networked and dis-tributed computing environments are subject to faults due tonetwork problems as well as failures in hardware and operat-ing systems. In the past, when simulations were executed in-dividually, error detections and corrections were possible byhuman control and intervention. With workflows and high-throughput calculations, this is no longer possible and auto-mated control mechanisms are required. Determining whethera specific simulation was correct or erroneous requirescontext-dependent information and judgment. Most advancedmaterials modeling platforms presently leave room for im-provement in error handling and fault tolerance.

Ease of Use

The ease of use of a software system can be gauged by thenumber, rapidity, and visual comfort of steps a user must taketo accomplish a task. If the task is simple andwell-defined, thenthe number of steps should be minimal and convenient. If thetask is complex, the user should have flexibility in defining theappropriate modeling approach and parameters. In any event,the user interface should be homogeneous in its automation,e.g., in the treatment of single-phase and multi-phase systemsor in the treatment of a single system or a large list of systems.

Theoptionsforeachstepshouldbeself-evident to theuserandthey should be presented in a form and language which is com-monly used in the specific discipline. Thehighdegree of special-ization inmaterials science and engineeringmakes the design ofeasy-to-use software platforms challenging. However, speciali-zation also implies a narrow focus, which couldmake it easier toconstruct protocols involving a small number of steps.

Typically, academic researchers require leading functionalityand access to a large rangeof computational parameters enablingthem to push the frontiers of research while industrial materialsengineers need well-tested computational protocols and param-eter settings toobtain a specific answer rapidly and reliably in thecontext of an overall engineering project. Few parameters areoften a good indicator of mature algorithms and programs.

Software engineers of user interfaces for smartphones aremasters in the creation of ergonomic systems, thus settingsimilarly high expectations for ease of use in other softwaresystems. However, user interface design for a smartphone issubject to very different constraints than those of highly spe-cialized materials modeling platform. The market for

smartphones is measured in billions while that for materialsmodeling software is about five orders of magnitude smaller.The possible investments in user interface design scale ac-cordingly. The functionality of smartphones like making aphone call, taking a picture, or getting directions to a restau-rant can be readily defined while ease-of-use for functionalitysuch as predicting the stress-corrosion behavior of a new alloyis rather different. Furthermore, “ease of use” for materialsscientists and engineers who are not modeling experts meansbeing able to perform specific but complex simulations in aneasy and straightforward way, e.g., use of property modulesand libraries of flowcharts. A full-time expert modeler hasother requirements, namely ergonomic design, intuitivemenus, and access to maximal functionality in the quickestways, avoiding multiple repeats of a certain action, e.g., bydefining loops in flowcharts and custom stages. Thus, ease ofuse remains a significant challenge for developers of e/a/mmodeling platforms and there is room for design diversityand products depending on the specific needs of target users.

Scientistsandengineersspendmanyhourson theircomputersand this time may increase in the future. Hence, ergonomicsshould not be forgotten and input from the medical scienceshould be taken into account in the design of user interfaces.

Standardization

Standards are the foundation of efficient communication andinteroperability. It is probably fair to say that today’s compo-nents for e/a/mmaterials modeling, for example different DFTprograms or forcefield codes, each have their own sets ofconventions and data representations, which makes their inte-gration in a common platform a tedious task. In software plat-forms developed since the 1980s, integration and unificationof different programs was achieved by creating data represen-tations and file formats which provides sufficient informationto generate program-specific input files. Post-processing thenconverts the various output formats into a common represen-tation. However, each software platform has had and still hasits own set of formats and limited practical standardization hasbeen achieved. However, several common file formats haveemerged to exchange structural data of atomistic models be-tween different software platforms. In this respect, significantefforts have beenmade by crystallographers, which have stim-ulated the developers of integrated software e/a/m platforms.

An important milestone in the standardization of molecularab initio calculationswas the introductionof the so-calledPopleGaussian basis sets. Thus, Hartree-Fock calculations on a spe-cificmoleculewith such a standardized basis set yield the sametotal energies close to machine precision independent of thespecific program. This is significant, as complex thermody-namic cycles can be constructed from values taken from calcu-lations performed by different authors with different programs.


In the case of solid-state calculations using DFT, such a stan-dardization has not yet occurred at the same level as in quantumchemistry. Even if two programs use the same approximation forelectron exchange and correlation, the resulting total energies stilldepend on many other computational choices such as specificpseudopotentials or numerical grids used for the radial functionsin all-electron methods, number of plane waves, and Fouriergrids. This lack of standardization hampers the exploitation ofcollections of DFT results and represents a major challenge forefforts such as the NoMaD DFT data storage and indexing pro-ject [38]. However, the issue of reproducibility in DFT calcula-tions is being addressed [19].

The situation in forcefield calculations is better than in therealm of DFT calculations. With a given forcefield, computedproperties obtained from different programs should result inidentical results if the same statistical ensembles and simula-tion parameters are employed. In practice, results from differ-ent simulations carry a statistical uncertainty, but they can beused in a common analysis.

Forcefield methods are used to simulate the behavior ofsystems such as molecular fluids and polymers. Computingproperties reliably for this class of materials hinges on correctstatistical sampling and, of course, on the quality of the un-derlying forcefield. For example, the result for elastic coeffi-cients of a single computation on a single amorphous polymermodel has limited meaning, whereas performing the samecalculation on 100 different models yields well-defined upperand lower bounds for such properties. Leading modeling plat-forms provide this type of statistical analysis.

The requirement for performing a set of connected simula-tions points to another requirement, namely that of reproduc-ible workflows. This is discussed in the next section.

Reproducible Workflows

Scripting of computational protocols has been in use for manydecades. This can be achieved using shell scripts or, moreconveniently, by graphical construction of flowcharts whereeach stage defines a specific operation. To ensure reproduc-ibility and re-usability of such flowcharts, it is important thatthese flowcharts are stored together with input and output datain a form which is readily accessible by other users.Furthermore, additional information such as the version num-ber of the software platform, the operating system, and com-puter hardware may be needed to ensure reproducibility, al-though the latter should not have a noticeable influence oncomputed materials properties, if the algorithmicimplementations are suitably numerically stable (Fig. 4).

Documentation

The complexity of scientific software requires documen-tation detailing the physical equations being solved,

explaining the algorithms and parameters, which influ-ence the quality of the solutions, and describing the prac-tical use of the software. Scientific publications related tothese programs are valuable as additional source, but theyare different from software documentation in purpose andscope. In fact, a complete users’ guide of e/a/m platformsis comparable in volume to a textbook. Documentation ispart of the software package and should be updated witheach new version of a platform.

Well-designed software systems offer context-sensitivehelp whenever the user needs specific instructions to accom-plish a certain task or needs to understand the available op-tions and implications of their choices. This form of documen-tation is extremely helpful in intuitive, direct-manipulationinterfaces as it relieves the user from switching to differentsources during a work session.

Computational Efficiency

The computation of materials properties can be numeri-cally very demanding and thus computational efficiencyis very important. Throughout the evolution of computa-tional materials science platforms, developers have hadto face the dilemma of finding the right balance betweencreating code which can be easily ported between differ-ent hardware platforms, and implementations, which seekthe highest performance on specific hardware architec-tures. In the 1980s and 1990s, vector supercomputersoffered unprecedented performance gains if special hard-ware features could be fully utilized. Cray Research pro-duced the most successful of this class of computers. Oneof the reasons for this success was the excellent balancebetween vector and scalar performance and the availabil-ity of powerful compilers, which did not require deepchanges in the computer programs to receive the benefitof the vector architecture. At present, similar argumentshold for parallel architectures. Given the steady progressin computational approaches and in view of the fact thatthe optimization of a large computer code for a specifichardware architecture can be a daunting and time-consuming task, developers are often reluctant to devotetheir time to the optimization of a specific hardware ar-chitecture such as GPUs. By the time such an optimiza-tion is completed, the field may have moved on and theusers may have a highly optimized code with obsoletefunctionality. Efficient compilers and software tools arethus essential for taking advantage of high-performancehardware. One has to keep in mind that various compu-tational approaches such as ab initio quantum mechanics,forcefield-based molecular dynamics, Monte Carlo simu-lations, and machine learning have quite different re-quirements for high-performance computing. Care must


be taken that each method benefits from advances inhigh-performance hardware.

Support

Support of a sophisticated scientific software system such asan e/a/mmodeling platform is needed on several levels, name-ly during installation and configuration of the software, in thepractical handling of the software, and in the correct and effi-cient scientific use. The first level is the domain of informationtechnology. Software running on a single machine typicallycan be installed fully automatically with minimum user inter-vention. Complex software, which is installed on differentmachines on a network with different levels of security andaccess privileges, may require support and customization. Thesecond level, the correct manipulation of the software caneither be covered by demonstrations, tutorials, user forums,online support, and training sessions.

In an industrial environment, the time of scientists andengineers is highly valuable. Thus, learning the effective useof a new software tool such as an e/a/mmodeling platform and

its integration in the engineering process represents a signifi-cant investment. To protect this investment, it is critical thatthe software environment is supported for many years in theform of updates, addition of new functionality, and migrationto new hardware systems.

Extensibility

Computational materials science continues to evolve, existingmethods are extended, new theoretical methods and algo-rithms are being developed, and new software systems arebeing created. Thus, the ability to extend and grow is an es-sential requirement for the long-term success of an e/a/m plat-form. This implies a clear modular structure of the softwarebased on a thoughtfully designed data model. Extensions canconsist, for example, of the addition of a new approach insolving Schrödinger’s equation, they can mean a newforcefield for molecular dynamics or Monte Carlo simula-tions, or the addition of a new coarse-graining for large-scalesimulations. However, extensions can also mean the additionof new paradigms such as high-throughput calculations.

Fig. 4 Flowchart of calculations of the mechanical properties of athermoset polymer. After building an amorphous model, the uncuredresin and cross-linking compound are equilibrated in a moleculardynamics stage. The subsequent Thermoset Builder constructs a cross-

linked model which is again equilibrated. The mechanical properties arecomputed in a final stage. This procedure implies loops over about 100different amorphous models. This flowchart can be stored, thus enablingreproducibility and re-usability (Color figure online)


Portability

Scientific software has a life span of many decades, as seen, forexample, by computational chemistry programs such asGaussian [20], which has its origin in the 1960s when com-puters had core memories measured in kBytes and were pro-grammed with punch cards. Today’s computing environmentsfor scientific and engineering applications are characterized bya mix of laptops, desktops, local compute clusters, and mas-sively parallel supercomputers usingWindows,MacOSX, andLinux operating systems. Cloud computing and hand-held de-vices are likely to play an increasing role in the future. Porting acomplex software system to a new operating system can begreatly facilitated if software is written in a machine-independent language and if any operating system-dependentparts are well isolated from the bulk of the code. On the otherhand, if a software system is tied to a single operating systemwith a graphical user interface relying on a legacy program-ming language, then the port to other environments can becomea daunting task. A costly re-write of the entire system may bethe only way forward.

Interoperability

The properties of materials depend on phenomena reachingover 10 to 20 orders of magnitude in lengths and time scalesand span essentially all branches of physics and chemistry. Agreat number of different approaches and software systemshave been developed to deal with this diversity on both thediscrete (e/a/m) and the continuum levels. Integration of hith-erto separate programs into unified platforms has been pur-sued since the 1980s, for example Insight II of BiosymTechnologies provided access to forcefield-based moleculardynamic programs (Discover) and a quantum chemical pro-gram (DMol) in a single platform. Another example isUniChem of Cray Research which provided interoperabilitybetween a semi-empirical quantum chemistry program(MNDO91), an ab initio Hartree-Fock program (CADPAC),and a molecular density functional code (DGauss). From asingle interface, a user could create an atomistic model, per-form a geometry optimization with a semi-empirical method,and use the output of this calculation seamlessly as input forHartree-Fock or DFT calculations.

More recently, the AiiDA open source project [21] aims atthe development of a platform for automation of calculations,input and output data storage, interoperability within workflows, and for sharing these contents within research commu-nities. Plug-ins for the solid state ab initio plane wave codeQuantum Espresso [22], the molecular quantum chemistrycode NWChem [23], Wannier90 [24], [25] for obtaining max-imally localized Wannier functions, and access to structuraldatabases such as the COD [26, 27] are being implemented.Furthermore, related activities can be observed in larger

research group and institutions, aiming at such platformsmainly for internal use.

This level of interoperability relies on a common platformthat uses a data model allowing the creation of program-specific input and the interpretation of the output of one pro-gram such that it can be used as input to other programs. Theunifying platform thus becomes a hub, which enables to plugin various programs. This model is relatively straightforwardto implement for quantum chemical calculations where a sys-tem is defined by atomic positions, element type, and possiblymagnetic moments.

On the other hand, the interoperability between programsusing classical forcefields is more complicated. For example,a Monte Carlo method for the calculation of adsorption iso-therms of a liquid in a nano-porous structure may use a unitedatom (UA) description of interatomic interactions, which can-not be uniquely mapped onto an all-atom (AA) descriptionusing a valence forcefield. Nevertheless, algorithms can befound to bridge this gap between different representations aslong as the UA and the AA forcefields can describe the entiresystem. This is no longer the case if a model includes, forexample, a metal surface covered with an organic polymer.The popular embedded atommethod (EAM) would work wellfor the metal, but is difficult to reconcile with a valenceforcefield, which is available only for the organic part of thesystem. The interoperability of programs and methods appli-cable only to a subset of a model is thus problematic.

Such incompatibilities become more complicated if onetries to build bridges between a quantum mechanical descrip-tion of part of the system coupled to a forcefield descriptionfor the surrounding of a chemically reactive area. Such so-called quantum mechanical/molecular mechanical (QM/MM) methods have been pursued since the 1980s especiallyin biomolecular systems. While conceptually appealing, thepractical treatment of the transition between the QM and MMdomains remains rather difficult. As mentioned earlier, themerits of multi-scale approaches for complex chemical sys-tems were recognized by the 2013 Nobel prize in chemistry.

Interoperability and coarse-graining open a host of issues,which have yet to be fully resolved. For example, whilelumping and clustering techniques seem logical in workingfrom the atomic scale to macroscopic phenomena, de-lumping techniques are required to allow interoperability inboth directions. Coarsening of an atomistic model is generallystraightforward, for example progressing from an all-atomdescription to united atoms, anisotropic united atoms, andtechniques of dissipative particle dynamics. However, creat-ing chemically correct atomistic models from a coarse grainmodel, for example in describing a grain boundary, is all buttrivial. Significant work lies still ahead to achieve interopera-bility on these levels.

In the foreseeable future, no supplier is likely to provide asoftware system covering the entire range of all possible


applications on all length and time scales in a single softwaresystem. Thus, enhanced interoperability between differentsoftware systems is essential for the overall success and im-pact of materials modeling.

Integration with Experimental Databases

“Numerical simulations without connection to experimentaldata is like clappingwith one hand”, asArthur J. Freeman usedto say. Thus, the integration of experimental databases addsgreat value to materials modeling platforms. This has been im-plemented, for example, in the MedeA® software platform,which includes leading crystallographic databases (ICSD,Pearson, Pauling,COD). These databases can be searchedwitha common interface, although the integrity and uniqueness ofeach database is preserved. The modeler can search, for exam-ple, for all compounds which contain Li and a transition metal,retrieve all such the structures with a few mouse clicks, andinitiate calculations such as the computation of the chemicalpotential of Li ions in all these compounds in a fewminutes.

Tracking of Metadata

As in experiments, the results of computations are meaningfulif additional information is associated with the computedphysical properties. This information includes the type ofequations underlying the models, the algorithms and compu-tational parameters being used, the program version, the time,and the scientist/engineer who performed the computations.Present computational platforms provide mechanisms to asso-ciate and store these metadata automatically with the results ofa computation. This aspect is particularly useful for work donein teams over a long period, where traceability and re-usabilityare of high value. Reproducibility might indeed be the mostvaluable aspect, because often one wants to build on previousexperience starting with an existing case for verification.

As computed material property data become integrated inan overall engineering process, it is important for legal reasonsand for the protection of intellectual property to pay attentionto the careful curation of metadata associated with computa-tions. On the other hand, an engineer may just be interested ina value and an error bar and might consider too many meta-data as distraction. This brings us back to the design of userinterfaces, where the perception of “best” differs between dif-ferent users.

Knowledge Management and Best Practices

As staff moves to different positions or retires, it is highlyvaluable for any organization that the knowledge and accom-plishments of these employees is retained and transmitted totheir successors. To this end, a goodmodeling platform shouldhave mechanisms to capture and retain the results of

computational investigations in a form that allows new em-ployees to retrieve and to capitalize on previous work.Concepts such as the JobServer of the MedeA® [28] softwareplatform automatically keep records of all simulations includ-ing input and output data together with flowcharts of simula-tion protocols as well as time stamps and user information,thus helping in the implementation of best practices for re-search and development.

From Academic to Commercial Software

A major part of e/a/m modeling software has been developedin academic research groups. Themajor driving force for thesedevelopments is scientific innovation and advanced function-ality. While typically very strong with respect to advancedfunctionality, present academic software often falls short inmany of the requirements discussed in the previous section,for example with respect to ongoing support andinteroperability.

Open source software is common in a variety of applicationareas including electronic structure calculations. The successof Linux is often cited as an argument for this approach. It isremarkable that the most successful solid-state electronicstructure program, namely VASP [29, 30], does not fall underthis category. While the source code of this program is acces-sible via a license, maintenance and new developments arehandled by a single university research group with clearlydefined responsibilities and ownership. Another program,namely CASTEP, is integrated in a commercial modeling plat-form while other electronic structure codes such as QuantumEspresso, Abinit, CP2K, and Wien2k are distributed either inthe form of the GNU General Public License or speciallicenses. In fact, there is a range of licensing models, e.g.,the Apache License, BSD license, GNU General PublicLicense, GNU Lesser General Public License, MIT License,Eclipse Public License, and Mozilla Public License. Each ofthese schemes has different implications for the transfer fromacademic research to industrial applications.

There is debate in the community as to which of theselicensing schemes is best. It is probably fair to say that thereis no simple answer to this question, because it depends onmany factors such as scientific and technological maturity, theorganizational structure supporting the development, the in-dustrial relevance, and the objectives of the key authors.

Publicly funded academic software is not always “opensource,” nor free. Moreover, there are additional aspects/differences between academic and commercial software, suchas generality, coverage, and robustness. Academic software isusually application oriented, e.g., proteins, ionic liquids, sorp-tion in specific solids, or the motion of dislocations in a metal.Additionally, academic software typically relies on develop-ments made by non-computer scientists, and optimization isusually not the leading objective. Commercial codes tend to


be more robust, more general in terms of methods and oper-ating systems, and better optimized.

Long-term support is often a key issue for academic soft-ware. In fact, experience in the domain of finite elementmethods, computational fluid dynamics, and TCAD for elec-tronic devices has shown that in the long run, the successfulindustrial deployment of modeling software is best served bycommercial providers. In the field of e/a/m modeling, we arewitnessing the transition from academic research to industrialapplication. In this context, efforts are being made to acceler-ate this transition. For example, the European Commission isfunding projects such as the EMMC-CSA [31] to enhance theindustrial uptake of material modeling by industry in the spiritof the ICME.

Illustrative Examples

Structural Materials

Zirconium Alloys

Zr alloys are primary structural materials in the core of nuclearpower reactors. Zr is a key element in the material due to itslow neutron absorption cross-section. Alloying elements in-cluding Fe, Ni, Cr, Sn, and Nb are added to improve mechan-ical properties such as yield strength and corrosion resistance.Fe, Ni, and Cr exhibit extremely low solubility and tend toprecipitate into secondary phase particles (SPPs). During irra-diation, the alloying elements are released from the SPPs andare free to diffuse and interact with defects in the material.

Zr alloys for nuclear applications have been subject to nu-merous, predominantly experimental, investigations.However, the number of computational studies is steadilygrowing. Ab initio DFT methods and EAM-based forcefieldsare valuable approaches that have been employed to studyirradiation-induced structural changes in Zr alloys [32, 33].With the development of experimental techniques such asatom probe tomography (APT), the material can be character-ized at the Ångström regime, allowing for direct comparisonsbetween experimental observations and data obtained from ane/a/m modeling platform. For example, APT studies haveshown that Fe segregates to metal grain boundaries and ring-shaped features of Fe have been observed in addition to smallclusters of not fully developed Fe precipitates [34]. The ring-shaped features can be interpreted as being due to segregationof Fe to dislocation loops.

All these experimental observations are reproduced withsimulations which can be performed using an e/a/m modelingplatform. Typical models are shown in Fig. 5. Simulationsusing an EAM forcefield developed by fitting to DFT datashow that Fe as an interstitial defect (as well as Ni and Cr) isa fast diffuser. The diffusion is anisotropic and axial diffusion

is faster than diffusion in the basal plane. Simulations addi-tionally show that Fe can form small intermetallic Zr-Fe clus-ters (not fully developed precipitates) and interact with pointdefects and extended defects such as dislocation loops. An Featom occupying a vacancy site may swap place with an inter-stitial Zr atom, thereby healing out point defects in the lattice.In the simulations, Fe atoms were found to decorate the rim ofirradiation-induced clusters of self-interstitial atoms, whichcan explain the experimental observation of ring-shaped fea-tures. DFT calculations show that it is energetically favorablefor Fe atoms to segregate to Zr grain boundaries in agreementwith the observations. In addition, the simulations show thatthe presence of Fe in the grain boundary is not detrimental tothe mechanical properties. Rather, Fe acts as a glue strength-ening the grain boundary.

This example shows that the contact area between experi-ments and simulations is extending to the behavior of individ-ual atoms. The empirical and theoretical methods complementeach other and a state-of-the-art, comprehensive e/a/m model-ing platform is a vital part of the synergistic approach.

Epoxy Thermosets

Among thewidespread use of polymericmaterials, epoxies areof great importance for light-weight high-strength materialsespecially in aerospace applications. The example presentedhere focuses on the dependence of the mechanical propertieson the choice of the resin. To this end, three typical resins areconsidered, namely diglycidyl ether of bisphenolA (DGEBA),triclycidyl p-amino phenol (TGAP), and tetraglycidyldiaminodiphenylmethane (TGDDM). These resins were curedwith 4,4′ diaminodiphenylsulfone (DDS). The structures of thecomponents are shown in Fig. 6 together with a 3D model ofDDS and a model of the cross-linked epoxy.

Between 50 and 100 different models for each compositionof cross-linked epoxies were constructed and the mechanicalproperties were computed using the highly accurate pcff +forcefield [45]. The underlying molecular dynamics simula-tions were carried out with the LAMMPS program [44] asintegrated in theMedeA® platform. Statistical averaging usingthe Hill-Wallpole method as introduced for simulations ofpolymers [37] leads to computed elastic properties showinga distinct dependence on the composition of the thermosetmaterial. The agreement with available experimental data isgood considering the uncertainties in the experimental data, ascan be seen from Table 1.

Functional Materials

Design of Zero-Strain Cathode Materials for Li-Ion Batteries

In Li-ion and solid-state batteries, the volume change of thematerials in the electrodes during charge and discharge is a


major source of degradation, which limits the lifetime of thebattery. While on the anode side, materials with close to zerostrain such as Li4Ti5O12 exist, there is still an ongoing searchfor light-weight zero- or low-strain cathode materials. Thiswork focused on manganese-based oxides crystallizing inthe spinel structure. Specifically, starting from LiMn2O4,alloying elements were sought such that the quasi-ternarycompound LiM1

xM2yM

3zO4 would minimize the strain in-

duced by alternating lithiation and delithiation. From a sys-tematic exploration of the composition space exploitingVegard’s law, the best candidates were found within the classLiMnxCryMgzO4. Their volume changes as a function of lith-ium content are displayed in Fig. 7 together with that of thebenchmark compound LixNi0.5Mn1.5O4. All calculations werecarried out using VASP [29, 30] in MedeA® with the PBEsolexchange-correlation functional [40, 41].

Thesemost promisingmaterialswere synthesized and charac-terizedbyX-raydiffractionaswellaselectrochemical techniques.The results were consistent with the ab initio predictions [42].

The DFT calculations also provided detailed insight intothe mechanisms resulting in a near zero-strain behavior. Itresults from the very different chemical bonding characteris-tics of Mg and the transition metal atoms as illustrated inFig. 8. While the Mg–O bonds tend to decrease with increas-ing Li concentration, the Mn–O bonds remain similar, where-as the Cr–O bonds tend to increase. As a result, the overallvolume of the crystal structure changes little upon chargingand discharging with Li ions.

This behavior is in close analogy to observation for thezero-strain mechanism for Li4Ti5O12, where local distortionsin the crystal structure likewise allow this material to keep thevolume nearly unchanged upon lithium insertion.

Fig. 5 Models of alpha-zirconium showing the ABAB stacking in thishexagonal structure on the left-hand side. The middle panel illustratesoctahedral and tetrahedral interstitial sites with the corresponding

pathways and transition states (TS). The right-hand side shows a typicalmodel which is used for molecular dynamics simulations of diffusion(Color figure online)

Fig. 6 Components of epoxythermosets depicted as 2Dchemical structures, a 3D modelobtained from a quantummechanical simulation, and amodel of a cross-linked epoxyused in forcefield-basedmolecular dynamics simulationsof the mechanical properties(Color figure online)


Liquids

Compared with solid materials, fluids offer a tremendous ad-vantage in connecting the macroscopic scale with atomisticsimulations. In many cases, macroscopic properties such asthe density, viscosity, and the vapor pressure can be obtaineddirectly from atomistic simulations involving only of the orderof a thousand molecules given proper statistical averagingover configurational space to obtain reliable results.

During the past decades, sophisticated molecular simula-tion methods have been developed, which enable the predic-tion of properties of fluids with remarkable accuracy and pre-dictive power [43]. This is illustrated by the computation ofthe normal boiling temperature of a sample of 100 differentorganic compounds (Fig. 9).

The computations are based on statistical thermodynamicsusing isochoric Gibbs Ensemble Monte Carlo simulations todetermine the equilibrium between the vapor and liquidphases as a function of temperature [44, 45]. The interactionsbetween atoms are described by a classical forcefield whichexpresses the total energy of a system as a summation ofdifferent terms which are described bymathematical functions

containing adjustable parameters. These forcefield parametersdescribe interatomic interactions such as the bond strengthbetween a carbon and oxygen atom as a function of the localchemical environment. They are obtained by fitting to datafrom first-principles quantum mechanical calculations on rep-resentative molecules as well as by calibration using a trainingset of experimental data. Once these forcefield parameters aredefined, they are used in systematic and transferable way toperform calculations for a large number of molecules. Thedevelopment of forcefield parameters for organic systemshas a history of many decades and today excellent forcefieldsfor such computations are available [46, 47].

The boiling points computed for 100 different organiccompounds agree very well with the experimental data, asillustrated in Fig. 9, with an absolute average deviation of1.4%. Note the wide range of different organic compoundsand the fact that all data points are bound within a relativelytight tolerance. This means that the approach has good predic-tive power and thus can be used in design cycles.

For all non-aromatic compounds, the anisotropic unitedatom (AUA) forcefield [48–56, 46] has been used. For thearomatic compounds, the TraPPE forcefield has been used[47]. The two forcefields used have been chosen for their highquality, allowing fast and high accuracy vapor-liquid-equilibrium calculations of the families of compounds includ-ed in this work.

Such computations of the pure compounds can be extendedto different temperatures, leading to a complete description ofthe vapor-liquid equilibrium or to different temperatures andpressures, leading to a complete description of the gas or theliquid phase, even at supercritical conditions.

Importantly, this approach can be also used for any mix-tures of the above compounds, thus providing vapor-liquidphase diagrams for a rather large design space, which cannow be explored computationally in an optimization process.

Many design and engineering problems involve not justsingle phases (solid, liquid, gas) but most often differentphases in contact. Forcefield simulations can be applied insuch systems, to study, for example, the sorption and diffusionof a molecular fluid into a micro-porous solid [43].

Properties of Molten Metals

Many types of liquids and their properties can be studied usingmolecular dynamics given suitable forcefields. For example,Fig. 10 illustrates a simulation cell that can be employed incomputing thesurface tensionofa liquidmetal, in the illustratedcase, copper, for which there are several suitable embeddedatom method (EAM) forcefields [57]. A range of propertiesmay be computed for such systems. Figure 10 shows the vari-ation of the computed surface tension as a function of tempera-ture and comparison with published experimental values forthis property. The details of the underlying surface tension

Fig. 7 Computed cell volume as a function of Li concentration intransition metal oxides with a spinel structure (Color figure online)

Table 1 Computed and experimental elastic coefficients of epoxythermosets with different resins

Resin Calculated bounds (GPa) Experiment (GPa)

DGEBA 3.49–3.53 2.4–3.2a

TGAP 4.42–4.45 4.396 ± 0.027b

TGDDM 5.18–5.19 5.103 ± 0.033b

aWhite et al. [38]; reported extents of reaction cover a relatively broadrange, 0.5–1.0; ∼300 K; dynamical mechanical analysisb Behzadi and Jones [39]; extent of reaction 0.93; 295 K; strain rate1.67 × 10−2 s−1


calculation are applicable to any liquid with the only provisobeing that adequate configurational sampling is feasible for thesystem in consideration [58–61]. For simple liquids, such con-figurational sampling can be achievedwith nanosecond lengthsimulations. For copper, the results of Fig. 9 show the precisionof the calculated property in comparisonwith experimental ob-servation [62]. The deviation of the simulated results from theirexpected straight line isminimal in contrast with the variabilityobserved in experimental studies. The general agreement be-tween computation and experiment apparent in Fig. 10 demon-strates that simulation provides a route to determining, under-standing, and optimizing the properties of such systems.This is

particularly valuable for situations where experiment is chal-lenging, for systemswith highmelting points, for example.Weemphasize that simulated properties are dependent on the qual-ity of the forcefield employed and that, even given a perfectforcefield, such calculations require adequate phase space sam-pling. The requirement for sufficient phase space sampling is aconsequence of the fact that evaluating the surface tensionamounts to computing surface-free energy implying the evalu-ationof thesystem’spartition function, a ratherhighdimension-al integral. However, as Fig. 10 emphasizes, where forcefieldquality may be relied upon, such methods provide substantialinsights into materials properties.

Fig. 9 Left: Families of compounds for which the normal boiling point is calculated; right panel: Monte Carlo simulation results (y axis) comparedagainst experimental data from DIPPR (x axis) for the normal boiling point temperature (Color figure online)

Fig. 8 Computed interatomic distances of the compound LixMn1.125Cr0.5Mg0.375O4 for increasing Li concentration (Color figure online)


Perspectives and Conclusions

Present software platforms for materials modeling on the levelof electrons and atoms have emerged from two major sources,namely molecular modeling systems used earlier in chemicalresearch and computer programs developed by researchers inthe field of solid-state physics and statistical mechanics. Theresulting software platforms are used in academic researchand their industrial deployment is growing. However, thisfield is presently characterized by groups of ab initio programswith similar functionality while there are still major gaps,which need to be filled.

Present computer codes originate predominantly from aca-demic research groups and thus are driven by the quest forleading research. Although some of these codes are integratedin e/a/m software platforms and are commercially supported,it is fair to say that fundamentally, these codes retain a mostlyacademic character. This reflects the actual state of the theoryunderlying ab initio solid-state computations. The present the-oretical underpinning such as density functional theory is veryuseful, but this level of theory is not yet fully satisfactory; newand better approximations to describe interacting many-electron systems are still evolving, which makes this fieldnaturally somewhat dynamic. Consequently, in academia,functionality is the major driving force rather than stability,robustness, error handling, standardization, and interoperabil-ity. Thus, programs that combine state-of-the-art scientificfunctionality with a good measure of the other attributesdiscussed above have emerged as clear leaders in their league.

On the level of forcefield-based codes for molecular dy-namics and Monte Carlo simulations, the picture is somewhatdifferent. Algorithmically, this level is quite mature, but theAchilles heel of this type of simulations is the quality andcoverage of the underlying forcefields or interatomic poten-tials. Forcefield developments have been pursued with greatvigor since the 1980 especially for biological systems such asDNA and proteins, which led subsequently to the extension ofthis methodology to the simulation of synthetic polymers and,

more generally, to the simulation of organic molecular sys-tems including liquids. Hence, simulations of such systemshave reached a remarkable accuracy, as demonstrated in thisreview for the case of the calculation of boiling points oforganic liquids. In contrast, the same cannot be said for thesimulation of inorganic systems such as metals, alloys, semi-conductors, and insulators. For these types of systems, theneglect of a quantum mechanical description of the electronicdegrees of freedom often encounters severe limitations, creat-ing a serious challenge for the simulation of complex systemsthat require models consisting of millions of atoms and manymillions of configurations.

Linking and coupling quantum mechanical domains withthose requiring forcefield simulations remains as a major chal-lenge for the creation of e/a/m modeling platforms. This goesbeyond the integration of various programs in a single soft-ware systemwhich is already accomplished by leadingmodel-ing platforms. The partitioning of a system into domains thatare handled by a forcefield or by a quantum mechanical de-scription is not automated and relies on the user’s decisions.Moreover, the coupling between these domains poses seriouschallenges, especially between metals and non-metallic solidsor fluids.

The creation and optimization of forcefield parameters “on-the-fly” is an intriguing perspective and, in fact, novel toolshave emerged that facilitate the generation of forcefield pa-rameters from ab initio training sets. Yet, the choice of theappropriate forcefield type and the optimization of the param-eters is far from being a fully automated and robust process.

The next level of linking in a comprehensive e/a/m model-ing platform can be summarized as coarse-graining/fine-graining. There is clearly a lack of tools which would facilitatethis task. As pointed out earlier, one needs to think not onlyabout coarse-graining, but one should also consider the in-verse, namely fine-graining, starting, for example, from amodel of a microstructure represented at the continuum leveland resolving the structure of dislocations and grain bound-aries on the atomistic level.

Fig. 10 Comparison of simulatedand experimental liquid coppersurface tensions as a function oftemperature. Experimental datafrom the work of Tahei et al. [54].The left panel shows a section ofthe model used in computing thesurface tension (Color figureonline)


Linking the microstructure to the processing and thechemistry of the material under consideration is yet an-other step, which should be part of an ICME implemen-tation. While major efforts are made at this level, thecommunication with the e/a/m levels leave ample roomfor improvements. Creating overarching software envi-ronments remains a daunting task. Extrapolating fromcurrent activities, the connection in multi-scale ap-proaches will be first implemented for specific applica-tions such as additive manufacturing and thus will re-main rather narrowly focused.

Integration into comprehensive software platformsand interoperability between platforms are certainly im-portant and highly useful, but the main bottleneck re-mains on the level of the theoretical and computationalapproaches.

This brings us to the issue of the economics of thedevelopment and support of materials modeling plat-forms in a still rapidly evolving field. In contrast tomolecular modeling in the pharmaceutical industry, ma-terials modeling is subject to quite different boundaryconditions. The goal of pharmaceutical research and de-velopment is very focused and well defined, namely thediscovery of novel molecules with specific therapeuticeffects. To support this effort, molecular modeling plat-forms serving this market have been developed and arecommercially supported. In contrast, the materials-related industry is extremely diversified in its goals witheach sector being relatively small, but very specialized.For example, the development of a high-performancesteel is quite different from the optimization of aSchottky barrier in a semiconductor; the optimizationof polymers for use in printed circuit boards requiresquite different knowledge and tools than the search forbetter solid-state electrolytes for Li-ion batteries, novelmaterials for data storage, or rare-earth free magneticmaterials. Each of these materials science problems re-quires quite different and very sophisticated simulationtools to make a valuable contribution to the engineeringprocess. However, many of these materials, for examplehigh-performance thermoelectric materials, represent on-ly a small fraction of the value of the final product, andyet a new material, such as cobalt-oxide cathodes, canenable extraordinary technologies, such as lithium-ionbatteries. The rewards for investment in advanced mate-rials discovery and process optimization can besubstantial.

There is also a desire to transfer research from industry toacademic groups, which either develop their own software orrely on open source software, which is adapted for the prob-lem at hand by indentured graduate students and post-docs. Insome circumstances, this can inhibit investment in buildingthe comprehensive e/a/m software platforms that fulfill the

criteria discussed earlier in this review. Hence, in addition tothe intrinsic scientific and technological challenges, there arealso economic hurdles which are to be overcome.

One perspective is the open-source paradigm combinedwith a licensing scheme which keeps the software and all itsfuture enhancements in the public. The initial investment ismade by public funding until the community of developersand users is large enough to sustain future developments.While such a paradigm has worked well for software such asthe Linux operating system, it is questionable if highly spe-cialized and sophisticated scientific software can be developedand sustained in the long run by this approach. The future ofthe excellent LAMMPS molecular dynamics program [36]will show how this paradigm will play out in the long run.

Another perspective is the rise of commercial softwarecompanies building integrated software platforms, which areprofessionally engineered and supported. As pointed out ear-lier, the investment for creating and supporting such integratedsoftware environments is very substantial because of the highdegree of sophistication and specialization in materialsscience.

The evolution of this field will likely represent a com-bination of these two perspectives. There is a place forsharing software in the early stages of conceptual andalgorithmic development in the spirit of academic re-search so that complex software systems can be createdwithout unnecessary and time-consuming duplication ofeffort. However, as software technology matures and itsinnovative appeal for academic research diminishes, theindustrial value of such software tends to rise as it movesfrom the stage of leading research to industrial produc-tion. Concurrently, the need for robustness and long-termsupport will increase. In fact, this is analogous to thedevelopment and transition of other research and devel-opment tools such as X-ray diffractometers, NMR ma-chines, and scanning tunneling microscopes. Leadingedge research at one stage becomes a production tool ina later stage and it is quite reasonable to assume that thesame trend will take place in the field of software plat-forms for materials modeling.

In view of the tremendous importance of advanced mate-rials and processes for the future of our civilization, the per-spectives for ICME are very bright. However, vision, commit-ment, investment, skill, and persistence will be necessary tomove this field forward and to reap its benefits for society. Thechallenges are many and the right expectations must beprojected. The embedding of e/a/m platforms in ICME frame-works will open unprecedented possibilities for the design ofmaterials and processes, but all participants in this processmust continue to support each other, including universities,government laboratories, funding agencies, software compa-nies, and industry. A Golden Age of ICME is ahead of us. Letus proceed with confidence!


Acknowledgements This project has received funding from theEuropean Union’s Horizon 2020 research and innovation programmeunder Grant Agreement No. 723867.

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