Software Methods for Sustainable Solutions

Software Tools for Development of Sustainable Solutions

Software Tools for Development of Sustainable Solutions

George Fitzgerald, Ph.D.

Introduction

• What do we mean by ‘sustainable solutions?’

• In this presentation we will focus on:– Alternative energy

– Catalysis

– Identifying and reducing environmental toxins

• What tools will we use?

© 2008 Accelrys, Inc. 2

• What tools will we use?– Molecular modeling like DFT & force fields

– Data analysis like recursive partitioning and neural networks

– Knowledge extraction tools – database searching and reporting

• These tools have also been used in research on– Carbon capture and sequestration

– Replacement of chlorofluorocarbons

– Improved crop production and protection

– Hypoallergenic formulations

– …

Outline

• Overview of software methods

• Solutions for Alternative Energy

• Solutions for Catalysis

• Solutions for Toxicology

• Demos


Why Use Modeling?

Screen

in silico

Select a

new candidateSynthesize

Test for

Effectiveness

Screen for

adverseAnalyze

Select a

new candidateSynthesize

Test for

Effectiveness

Screen for

adverseAnalyze


• Typical workflows with and without modeling.

• Modeling accelerates the discovery process by allowing you to test materials before going into the lab

– Modeling faster than experiment

– Though not 100% accurate, modeling can distinguish good leads from bad

– Modeling lets you extract trends, understand what contributes to a “good” lead

• Modeling allows you to focus your efforts on only the most promising leads, saving time and expense

Fast loop!

Virtual Screening

• Virtual screening is the cornerstone of in silico drug discovery

• Allows researchers to effectively screen drug design space to identify most promising structures

– reduces the size of a chemical library to

be screened experimentally: O(106) to

O(10)

• Improves the likelihood of finding

Quick & dirty calculations


• Improves the likelihood of finding interesting structures

– systematic screening

– screen possible design space before

synthesized

• Saves time and money

– computational evaluation is faster and

much less expensive than experimental

testing

Now possible to apply techniques to materials scienceNow possible to apply techniques to materials science

Sophisticated calculations

Experiment

• Quantum– Solution of the Schrödinger equation

– Good results for structural, electronic, optical

properties

– Necessary for systems with bond-breaking,

reactions and catalysis

– Limited to <1000 atoms

HΨ = EΨ

Modeling & Simulation Overview


• Molecular– Approximate atomic forces with ball-spring

model, charges, vdW forces

– Good results for structures, interaction

energies, miscibility, solubility, adhesion

– Diffusion, permeation, membrane transport

– Useful up to around 10,000 atoms

• Mesoscale– Groups of atoms represented by beads– Empirical forces between beads account for

effects such as viscosity– Micelle or vesicle formation– Emulsions, kinetics and properties– Polymeric microspheres– Applicable to 100,000 atoms

Modeling & Simulation Overview


• Bulk– Finite element models

– Requires reliable parameters, built up from

more accurate methods or determined

empirically

– Structural properties for bulk-scale systems

– Elastic constants, thermal expansion, gas

permeability, crack propagation

Overview of Statistical Methods

• Goal: analyze the results of many calculations to– Extract trends

– Gain understanding of which parameters are important to

performance

• QSAR (Quantitative Structure Activity Relationship)– Assume a relationship exists between structure and function

– Use things are easy to calculate to make predictions about things

are hard


are hard

– Ex, toxicology models

– Can be quite quantitative when fit to large data set

• Data reduction– Simplify the way you look at many variables

– Correlation matrix

– Principle component analysis

• Cluster analysis– Define similarity based on some criteria

– Nearest-neighbor analysis

– Hierarchical clustering

Overview of Reporting

• Sometimes the best thing you can do is just look at your data– Do good results tend to one side or the other?

– Can I spot an obvious minimum or maximum?

– Does one result stand out?

• When you have lots & lots of data you can use interactive reports

– One view gives high-level overview

– Click on a point to zoom in and get detailed information


– Click on a point to zoom in and get detailed information

– Create comparative reports of your selected results

Alternative Energy Examples

• Fuel cells– Stability of polymer membranes

– Hydrogen storage

– Oxygen activation catalysts

• Biodiesel: fat to fuel

• Batteries: extending lifetime


• Other examples– Gas to liquid

– Coal to liquid

– Improved combustion

Anatomy of a Fuel Cell

• Components we can model– Hydrogen storage

– Hydrogen activation

– Proton exchange

– Oxygen activation

• Applications– Power Stations


– Power Stations

– Space Vehicles

– Home and Business Power Supply

– Transportation (buses, trucks, cars,

motorcycles…)

– Portable Applications

• Laptops, cell phones etc.

• Military

Polymer Membranes

• Polymer membranes used in both hydrogen and direct methanol types of fuel cells –PEMFC, DMFC

• Proton conduction membranes usually consist of polymer with covalently-bound acidic groups such as SO3H or CO2H

• Traditionally based on Nafions (Dupont® perfluorosulfonate polymers)

• Some problems with Nafion include:– Poisoning of catalyst. Could be reduced by operating at higher temps (120-200°C)


– Poisoning of catalyst. Could be reduced by operating at higher temps (120-200°C)

– Water must be present; Dehydration at higher temperatures (>~80°C) leads to loss of

proton conductivity

– Expensive

– Diminished mechanical stability at higher temperatures

– Undesirable permeability to methanol (DMFCs)

• Need new polymers to overcome limitations and create less expensive and more efficient cells capable of running at higher temperatures

AcknowledgementsJames Wescott (Accelrys)Lalitha Subramanian (Accelrys)

Steps to Modeling PEFC

• Pick one problem at a time to start out

• Create appropriate model

• Decide on appropriate modeling methods

• Validate against known results before doing predictive modeling!

• Systematically change materials to optimize properties

• Ultimate goal: create a PEFC membrane that is more stable with


• Ultimate goal: create a PEFC membrane that is more stable with respect to moisture

• Initial goal: predict structure as a function of water content– Experiment only probes surface structure, or has lead to ambiguous results

– Need the structure in order do any other modeling

– Maybe looking at the structure will give us ideas

• What model?– Amorphous Nafion, large periodic cell

– Morphology of Nafion/water system has structures on the order of 10’s of nm

– Requires 1000’s of atoms

• What tools?– Mesoscale model is needed because of the size

Molecular Structure of Nafion®

Non-polar Polar

N P

CF2

CF2

CF2

CF2

O CF2

CF O CF2

CF2

SO3H

z

y

x

n


2

CF3

2 2 3z

Atomistic model Parameterization “Bead” model

Nafion Calculations

• Program: MS MesoDyn– Uses mean-field density functional theory

– Coarse-grained method for the study of complex fluids, kinetics, and their

equilibrium structures

• Considers interaction parameters between “beads”

• Parameters derived from force field calculations or obtained from literature


from literature

• Start from initial guess structure and allow to evolve until stable

Atomistic model Parameterization “Bead” model

Mesocale Modeling Results

λλλλ = 2


Mean squared difference of concentration

from average concentration, i.e., a measure

of phase separation.

λλλλ = 8

Mesoscale Modeling Results

• Phase separated micelles filled with water, surrounded by side chains containing sulfonic groups, and embedded in the fluorocarbon matrix starting around λλλλ = 4

• General agreement with the experimental morphologies in terms of– Distribution and shape of water domains

– Quantitative prediction of 2–5 nm cluster sizes

• Next steps


• Next steps– Study dynamic processes, e.g., hydrate – dehydrate

– Model proton mobility

– Change membrane components systematically and predict performance

• Acknowledgements– James Wescott (Accelrys)

– Lalitha Subramanian (Accelrys)

Hydrogen Storage Challenges

• Seek a material that will allow on-board storage of Hydrogen (as H2, CH4, CH3OH, etc.)

• Engineering challenges– Target driving range of ≥≥≥≥ 300 mi– Must also meet cost, safety, etc. standards

• Materials Science Challenges– High H storage capacity: 6 wt% by 2010; 9 wt% by 2015


• Materials Science Challenges– High H2 storage capacity: 6 wt% by 2010; 9 wt% by 2015– Low device weight– Rapid discharge/recharge – Durable for many discharge/recharge cycles

Hydrogen Storage Materials

• Metal hydrides– Alanates

– Destabilized binary hydride alloys

– Lithium amides

– Nanoscale lithium nitride materials

• High surface area sorbents

• Chemical storage– Sodium borate

– Liquid chemical hydride

– Magnesium hydride slurry

• New materials– Conducting polymers


• High surface area sorbents– Graphitic materials

– Nanostructured carbon

– Conducting polymers

– Metal organic frameworks

– Clathrates

– Perhydrides

This list is not comprehensive

Steps in Modeling H2 Storage

• Focus on one problem– Type of material (e.g., metal clusters)

– Form of hydrogen (e.g., H2)

– Particular challenge (e.g., binding energy, loading capacity)

• Create appropriate model, .e.g.,– Generally, you will be working with a team that has already decided on a class of

material

– Periodic super-structure or cluster?


– Periodic super-structure or cluster?

– Make approximations in size?

• Larger model → more accurate

• Smaller model → faster calculations

• Select appropriate theoretical approach– Chemisorption needs QM-based method

– Physisorption can use force fields

– Time-evolution (diffusion) very expensive to do with anything but force fields

Aluminum clusters for H2 Storage

• Magic cluster sizes, i.e. those with closed-shell electron numbers, are:

N= 2, 8, 18, 20, 34, 40, 70, 112 …

– Al13 cluster is only one electron short from ‘magic’

• Experimentally and theoretically both have been found to be especially stable


• Might these work for H2 storage?

…Acknowledgements

Alexander Goldberg (Accelrys)

Irene Yarovsky (RMIT)

Goals of this work

• Long term:– Develop a porous solid of Al nanostructures for use in H-storage

• Short term: – Model stable geometries of atomic and molecular hydrogen adsorbed on Al clusters

– Calculate adsorption capacity of Al clusters

– Calculate adsorption-desorption barriers

– Estimate mobility of H on the surface

– Study the cluster size effects on H adsorption

• Method


• Method– QM-based approach

– Density Functional Theory (DFT)

– Determination of energy minima

– Determination of transition states and energy barriers

• Model– Single nanoclusters of Al13

Two isomers of (Al13H)2 from Alonso, et al., Nanotechnology 13(2002) 253-257.

Al Cluster Calculations

• Density Functional Theory using MS DMol3

– Fast implementation of DFT

– Works for molecules and periodic solids

• DNP basis set – equivalent in size to Gaussian 6-31G**

• Exchange-correlation functional: BLYP

• TS search using LST/QST method of Halgren and Lipscomb: Chem. Phys. Lett. 49, 225 (1977)


• Construct clusters starting from periodic Al metal models

• Approach validated by comparing to experimentally determined LUMO and IP of Al13-

and Al13H

Potential Energy Diagram

Potential

Energy

Distance

H-H bond breaking

Al-H bond formation

Physisorption well


Al13

H H

Physisorption well

Chemisorption well

Al13

H H

Potential Energy Diagram

Potential

Energy

Distance

H-H bond breaking

Al-H bond formation

Physisorption well-5

0

5

1.59

En

erg

y, k

cal/m

ol

Al13+H2 energy


Al13

H H

Physisorption well

Chemisorption well

Al13

H H

-20

-15

-10

-5

En

erg

y, k

cal/m

ol

separation distance, Å

Chemisorption14.24 kcal/mol

Physisorption3.6 kcal/mol

• The reaction Al13 + H has no potential barrier

• The reaction Al13 + H2 has a small potential barrier

• Al13 is a potential storage medium!

• Future plans

Hydrogen Storage Conclusions


• Future plans

– Effect of element substitution

– Crystals of clusters

– Diffusion rates

– Thermal stability

Challenges in biodiesel fuel development

• Free fatty acid (FFA) content can result in soap formation and reduced yield of biodiesel (methyl ester) upon reaction with alkali catalysts.

• Soaps may allow emulsification that causes the separation of the glycerol and ester phases to be less sharp.

• When FFA levels are above 1%, it is possible to add extra alkali catalyst.

• For feedstock containing 5-30% FFAs, one needs to convert the FFA to biodiesel or the overall conversion will be low.


the overall conversion will be low.

Biodesiel Production Technology, J. Van Gerpen, B. Shanks, R. Pruszko, D. Clements, G. Knothe,

NREL/SR-510-36244, July 2004.

Options for High FFA

• Enzymatics methods: Expensive and not used commercially

• Glycerolysis: Requires high temperature and is slow.

• Acid catalysis: Esterification of FFAs is fast, but transesterfication is slow. Water is produced which can halt reaction.

• Acid catalysis followed by alkali catalysis. Acid catalysis is used for pre-treatment. When the FFA content is near 0.5%, alkali is added to convert triglycerides to methyl esters


esters

• Goal: predict fatty acid volume (FAV) as function of process conditions

• Method– Apply statistical methods (neural networks and genetic function algorithms) to optimize

process conditions (reaction time, methanol-to-oil ratio, H2SO4 concentration)

Biodesiel Production Technology, J. Van Gerpen, B. Shanks, R. Pruszko, D. Clements, G. Knothe,

NREL/SR-510-36244, July 2004.

Statistical methods to optimize biodiesel production

• Does not require much computational power

• Requires “lots” of data, 5 data points/parameter or more

• Once you create a model, easy to test 1000’s of combinations

• Start with systematic grid of data– Fit to a function (GFA or NN)

– Search parameter space for optima


“Prediction of optimized pretreatment process parameters for

biodiesel production using ANN and GA”, Rajendra, P. C. Jena,

H. Raheman, Fuel 88 (2009) 868–875.

Applying statistical methods to optimize a function

• Development of statistical methods and process parameter optimization via graphical workflow tools

• Define input variables (reaction time, etc)

• Define dependent variable (FAV)

• Number of terms in the model

• Model can be saved, reused, sent to collaborators

• Workflow can set up systematic search of grid, identify optima


• Workflow can set up systematic search of grid, identify optima

Lithium Ion Batteries and SEI Film Formation


• The electrolyte typically consists of one or more lithium salts dissolved inan aprotic solvent with at least one additional functional additive

• Additives are included in electrolyte formulations to increase thedielectric strength and enhance electrode stability by facilitating theformation of the solid/electrolyte interface (SEI) layer

Acknowledgements

Ken Tasaki (Mitsubishi Chemicals Inc.)

Mathew Halls (Accelrys)

Computational resources: HP

Lithium Ion Batteries and SEI Film Formation

1 e- decomposition scheme

2 e- decomposition scheme


• Initiation step leading to anode SEI formation is electron transfer to theSEI forming species resulting in a concerted or multi-step decompositionreaction producing the passivating SEI layer at the graphite-electrolyteinterface

• Important requirements for electrolyte additives selected to facilitategood SEI formation are:– higher reduction potential than the base solvent

– maximal reactivity for a given chemical design space

– large dipole moment for interaction with Li

Modeling Battery Additives

• Choose one aspect– Identify compounds that will form SEI by breaking down before the electrolyte does

• Select models– Library of candidate structures based on known additives with modification of pendant

groups


• Choose computational approach – Modeling entire SEI formation is hard

– Requirements for a good additive are easier to calculate:

• Increased reduction potential correlates with a lower LUMO or higher electron

affinity (EAv)

• Measure of stability or reactivity is the chemical hardness of a system (η)

• Larger dipole moment leads to stronger dipole-cation interactions (µ)

– QM required for these properties

• Work by Chung et al., has shown that semiempirical method is effective

Anode SEI Additive Structure Library

R1

O

R2

O

R3

R4

O

X

X

Z

X

X

X

X

X

Z

XXX

XX

Z

X

X

X

Z

X

XZ

XX

X

X z1


• Cyclic carbonates, related to ethylene carbonate (EC), are often used asanode SEI additives for use with graphite anodes

• To explore the effect of alkylation or fluorination on EC-based additiveproperties an R-Group based enumeration scheme was used to generate aEC-based additive structure library (7381 stereochemically uniquestructures)

XX

X = F or H

X z1

Anode SEI Additive Descriptors

• Increased reduction potential correlates with alower LUMO energy value or a higher verticalelectron affinity (EAv)

• Measure of stability or reactivity is the chemicalhardness of a system (η)

• Larger dipole moment leads to stronger dipole-cation interactions (µ)

• Lots of calculations

ELUMO, EAv

µ


• Lots of calculations

– Requires neutral, cation, anion for each molecule

– 1000’s of molecules

– Automate computation and analysis with workflow

management tools

µ

Anode SEI Additive Library Results

• No one material satisfies all 3 simultaneous objectives

• Multi-objective solutions represent a trade-off

• Pareto-optimal solutions are defined as a set of solutions such that is not possible to improve one property without making any other property worse


• For anode SEI additives Pareto optimal solution is the structure shown

Li-ion Battery Summary

• The generation of virtual structure libraries can be used to explore materials design space

• Advanced materials modeling workflows can be captured in pipelined protocols enabling the analysis of virtual materials libraries

• Combination of molecular modeling and data analysis can identify


• Combination of molecular modeling and data analysis can identify leads efficiently

• Acknowledgements– Ken Tasaki (Mitsubishi Chemicals Inc.)

– Mathew Halls (Accelrys)

– Computational resources: HP

Z1

En

erg

y

Z2

Ea,0

E E

Without

Catalyst

Ea,0

E E

Catalysis

• Catalysis is critical to modern chemical industry– 60% of chemical products

– 90% of chemical processes

• A good catalyst will– Make the reaction proceed faster & at lower T

– Make the reaction run at lower temperature

– Increase yield

• Catalyst lowers the reaction energy barrier, increases rate


Reaction CoordinateE

ne

rgy

With

Catalyst

R

A*P*

Ea,1 Ea,2

∆HR

Ea,1 Ea,2

P

increases rate

• Modeling can provide– Reaction energies ∆HR

– Energy barriers Ea

– Structure of intermediates

• Modeling allows you to explore in silico– Effect of catalyst composition

– Effect of poisons or promoters

– Efficiency of catalyst for alternative R

Introduction to iCatDesign

• Goal: develop combined computational and experimental methods for improved catalysts for oxygen reduction reaction (ORR) in fuel cells

• Collaboration with CMR Fuel Cells and Johnson Matthey

• Co-funded by Technology Strategy Board's Collaborative Research


• Co-funded by Technology Strategy Board's Collaborative Research and Development Programme

Adsorption and activation energies: ORR

E

E0=E(O2+*)

ETS=E(O*-O*)


Reaction coordinate

E1=E(O2*)

E2=2E(O*)

Ediss=E

2-E1

Eads1=E

1-E0

Ea=E

TS-E1

Eads2=E

2-E0

Eads1=E

1-E0

iCatDesigniCatDesign

Models

• Approach:

– DFT calculations using plane-wave + pseudopotentials

– 5-layer slabs, with 2 bottom layers ‘frozen’

– Calculate reaction barrier for each combination of base &

promoter element

– Substitute 3 promoter atoms at a time

• Initial step: find alloys that bind atomic oxygen more tightly

– Observation: center of d-band correlates with Oxygen

absorption energy and is faster to calculate


absorption energy and is faster to calculate

• For Pd3Co in 3 layer model, there are 220 unique structures

– Most stable corresponds to all Co atoms in the 3rd layer

– Other configurations contribute to ensemble average

– How do we keep track of all the calculations?

Config gi ∆E=E-E0, eV exp(-∆E/kBT), T=300K

A0B0C3 4 0.0000 0.643

A0B1C2 24 0.122-0.128 0.986E-02

A0B2C1 24 0.105-0.127 0.0154

A0B3C0 4 0.016 0.331

Total 0.9993

High Throughput Workflow

Calculate stable surface alloy

structures

Oxygen reduction descriptors- D-band centre positions

- Electron work functions …

Less expensive calculations: perform many


Adsorption energies Activation energies

Potential Candidate?

Predict reaction ratesCompare to experiments

More expensive calculations: perform fewer

Database

of results

Chemical reactivity and mechanical strain

• What causes change of catalytic activity upon alloying?– Electronic properties of base metal are important

– Base metal admixture results in mechanical surface strain, which in turn affects its

catalytic activity

• d-band center and work function are analyzed as functions of unit cell parameters

• Using band structure as a guide– d-band should overlap O2 HOMO

– Plot of band center shows how to change unit cell parameter to reposition d-band


– Plot of band center shows how to change unit cell parameter to reposition d-band

– Alloy with promoters that push the lattice parameter in the right direction!

iCatDesign

Pt CuPd

Pt: d-band overlaps O2

HOMO at equilibrium or

smaller values

Pd: need to compress

lattice to improve d-band

Cu: need to expand lattice

to improve d-band

Summary of iCatDesign

• iCatDesign project resulted in developments of new solutions

– Streamlining high throughput QM calculations

– Analysis and reporting of large amount of calculated data


– New science in Fuel Cells developments and in heterogeneous and electro- catalysis

in general

iCatDesign Acknowledgements

• Primary researchers– Accelrys: Jacob Gavartin, Alexander Perlov, Dan Ormsby

– Johnson Matthey: Sam French, Misbah Sarwar

– CMR Fuel Cells: Dimitrios Papageorgopoulos

• Assistance from – Amity Andersen

– Alexander Perlov

– Alexandra Simperler


– Alexandra Simperler

– Victor Milman

– Patricia Gestoso-Suoto

– Gerhard Goldbeck-Wood

– Julian Willmott

– Mark Faller

– Jaroslaw Tomczak

– Stephane Vellay

– Richard Cox

Environmental Chemistry and Toxicology Overview

Some challenges facing industry today:

• Inefficiency in collecting analyzing and acting on disparate data

• Determine toxicity of new compound– Compile physico-chemical and toxicity data with a minimum of additional testing

• Determine if a new compound will break down to toxic by-products

• Reduce animal testing


Environmental Chemistry and Toxicology Regulation

• Existing U.S. regulations– OSHA – Occupational Safety and Health Administration

• Permissible Exposure Limits, Hazard Communication

– RCRA – Resource Conservation and Recovery Act

• Subtitle C – “Cradle to Grave” chemical tracking

– CWA – Clean Water Act

• Requires permitting of point source polluters including industrial facilities

• European Community REACH


• European Community REACH– Registration, Evaluation, and Authorization Chemicals

– Guiding principle: “No data, no market.”

– Reduce unnecessary experiments using QSAR and read-across

– Protect human health and the environment from potentially

harmful chemicals and make manufacturers and importers

responsible for managing the risks of the chemicals

• Revision Looms For U.S. Chemical Law (C&E News, June 9, 2008)– 1976 Toxic Substances Control Act (TSCA) allows EPA to request toxicity data

– EPA has no resources or mechanism for collecting this data

– Thousands of high-production-volume (HPV) chemicals have no toxicity data

– Congressional bills S. 3040 and H.R. 6100 introduced in May would require toxicity data

*

* Environ Health Perspect. 2008 March; 116(3): A124–A127

Solutions for Toxicology

• Statistical data mining

• Substructure searching

• QSAR-based tools for – Predictive toxicology

– Degradation products

– ADME products


– ADME products

• Data storage and retrieval

• Deployment– By combining modeling tools with Pipeline Pilot, a simple web page is

presented to a user who enters the structure or name of the

compound. That input is seamlessly past to the modeling tool that returns

related compounds and their known and predicted toxicity.

Collect, Analyze, Act on Data

• Interactive reports– Ability to analyze specific components over time

– Add all components that contain a specific regulated chemistry

• Molecular substructures– Automatically search for

any compounds that

contribute to a

regulated endpoint

• Higher level reports


• Higher level reports– Principle compounds

in effluent

• Lower level reports– Within a given time

frame what did the

raw analytical results

look like

Identify Degradation Products

• Challenge: For a given set of compounds identify the likely breakdown products– Generally monotonous, prone to oversights

– Specialized reactions may be missed

• ECT-encoded biodegradation pathways– Automatically and systematically process compounds

– Any unique pathways can be encoded so the reactions are never overlooked


Identify Degradation Products

• Beyond first-level breakdown products– View the chemical breakdown process with references

– Explore toxicity models

– Expand to multiple levels of products


Complete Aerobic Biodegradation of Aspirin

OO

O CH3

O

OO

OH

OH

OH

OO

OH

OH

OH

OH

OH

••

•

•

•• ••


Mutagenicity •

Hepatotoxicity •

Fathead Minnow •

Solubility •

O

O

O

O

O

O

O

O

O

O

O

CH2

O

O

CH3

O

O O

O

O

O O

O

O

CH3

OO

O

O

O

O

O

O

O

O

O

O

O OH

O

O

O

O

O

O

O

O

O

••

••

•••

•

••

• •••

••

••

••

Predictive Analysis

• Get a comprehensive overview of physical, ADME, and toxicological properties– Easy-to-interpret graphical representations showing both calculated properties and business rules

– With appropriate authority easily update business rules in response to changing regulations or

environmental conditions


Detailed Bayesian Model

• Get a full understanding of the model used to predict end-points or other properties– Automatic “learning,” QSAR models

– Optimum prediction space (OPS) analysis to ensure results are relevant


Other Reports: Facility Reports

• Summary reports that can be live and include historical trends

• Drill-down capabilities– Summary data from multiple facilities or teams

– Reports for individual

chemists

– Detailed reporting

and analysis on

each compound with

all assay results


Summary

• Environmental health & safety regulations will force companies to maintain accurate records of materials, screening results, effluents, etc

• Companies will be responsible for demonstrating the safety of compounds

• Simple tools like databases and web reports make it simple to keep track of data

• Predictive tools based on QSAR make it possible to predict activity of new compounds


Conclusions

• Software tools have already contributed to easing impact on the environment– Fuel cells

– Batteries

– Biomass conversion

– Catalysts

– Toxicology

• Tools include


• Tools include– Conventional molecular modeling

– Statistical analysis

– Reporting

• Software is becoming easier & easier to use, but …

• Applying some careful thought ahead of time to get the most out of your calculations