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1© ASM International
2© ASM International
• Computational Materials Data Network
• Materials Genome Initiative (MGI)
• SMDDP project launch and implementation
• Live database demo
• Lessons in database development
• Future work & potential follow-on projects
• Summary
Webinar outline
3© ASM International
ASM is a trusted
provider of critical
materials data and
a natural choice
to support the
materials data
community in its
efforts to establish
best practices for
capturing, organizing,
and sharing digital data.
Data heritage
4© ASM International
In 2012, ASM launched the
Computational Materials Data
Network to assist the MGI/ICME
community with its computational
materials data challenges and needs.
Adapted, Office of Science and Technology Policy
CMDN focus: Digital data
5© ASM International
• Advance materials data
management practices and
techniques
• Serve as a hub for the
collection and dissemination
of materials data
• Support the MGI/ICME
community
• Enable faster materials and
process innovation
Goals and objectives
6© ASM International
Society strengths
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• Develop new materials faster
• Revitalize U.S. manufacturing
New tools, new practices,
new ways of working together
Adapted, Jim Warren, NIST
Supporting data-driven innovation
8© ASM International
Source: Jim Warren, NIST
Data management challenge
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Data repository
Source: Jim Warren, NIST
Increasing organization
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Open curated
repositories
Source: Jim Warren, NIST
Increasing scale
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SMDDP Repository
Adapted, Jim Warren, NIST
Demonstration project
12© ASM International
• Establish well-pedigreed and curated demonstration datasets for non-
proprietary metallic structural materials over multiple length scales.
• Work with NIST and the materials data community to develop
materials data schema and ontologies.
• Develop and carry out a series of test problems that represent
relevant use cases for the repository.
• Make data open to the materials data community for use in data
analytics, modeling, and educational activities.
• Actively engage the materials data community and widely
disseminate the findings from the project.
• Develop and implement data capture and curation procedures that
can serve as models for other data repositories.
SMDDP project objectives
13© ASM International
ASM InternationalCMD Network
NIST
Kent State UniversityCenter for Materials Informatics
Granta Design/MDMI
Georgia Tech
• Hosting and curation
• Project management and outreach
(Nexight support)
• Data acquisition for “upstream” data
• Schema and ontology development
• Test and evaluation support
• Open access repository development and
support
• Database structure and development guidance
• Import and export interface development
• Aluminum sample processing
• Microstructure and mechanical data
measurements
Project roles
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Phased rollout
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SMDDP data path
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http://www.asminternational.org/web/cmdnetwork/projects/structural-materials/project-homepage
Project homepage
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https://materialsdata.nist.gov/dspace/xmlui/
Accessing the SMDDP repository
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https://materialsdata.nist.gov/dspace/xmlui/handle/11256/419
Data collections
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SMDDP database homepage
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Lessons learned
• Best practices for capturing and sharing data
– Chart a strategic course and identify critical pathways
– Spend time upfront on database organization and schema development
– Make sure others can repeat your work by providing sufficient pedigree and provenance
– Employ standard file formats
– Don’t overlook data citation and licensing
• Limitations of traditional search technology
– Benefits of ontologies, registries, and semantic search
• Currently available data are not MGI/ICME friendly
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Critical pathways
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Critical pathways
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Organizational refinements
• Bottom up organization; hub-and-spoke
linkages
• Similar pattern followed at each level
• Elements
• Systems
• Alloys
• Records renamed to create more
consistent pattern
• [Content description] ([Source label])
• Reduced unnecessary levels in tree
structure hierarchy
• More consistent treatment of sources
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• Relating the information reduces any
need to repeat data
• Selection of related data is simplified
• Experimental and computational data can
be more easily identified and compared
Relational database schema
Source: Tom Searles, MDMi
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• Material history is the foundation for understanding and employing
experimental and computational data
– Provides immediate comparison between experiment and simulation, enabling
correlation and model prediction assessment
• Capturing this information facilitates repeatability and reproducibility
– Enables re-assessment of a model’s accuracy as the experimental data set grows
• Missing traceability devalues data
– Renders data unusable beyond initial scope
– Limits modeling and validation of data
• No aspects of material pedigree and provenance should be overlooked while
collecting and storing material information
– Determining this information post-project is often extremely difficult if not
impossible
Traceability: Pedigree & provenance
Source: Tom Searles, MDMi; Steve Arnold, NASA-GRC
26© ASM International
• Consistency is key
– Keeping a standard format reduces the effort necessary to
collect and store data
• Preference is standardized ASCII or Excel files
– Enables rapid collection of information
– These formats are preferred by most
– GRANTA MI allows for rapid data collection in these formats
• Data should be written directly into the data repository
File formats
Source: Tom Searles, MDMi
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Ontologies and semantic search
Source: Sam Chance, iNovex, matonto.org
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If a community uses a shared vocabulary to annotate its
data, then interoperability can be greatly enhanced by
eliminating ambiguity – e.g., resolving synonyms (elastic
modulus, Young’s modulus), connecting properties to
relevant test standards (ASTM E111).
• The CMD Network is contemplating a pilot project for an open materials
vocabulary in connection with the NIST-ASM Structural Materials Data
Demonstration Project.
• ASM has thousands of ASM Handbook terms and definitions.
• We are engaged in discussions with ASTM about potential approaches to
harmonizing ASM and ASTM terms and definitions.
Common materials vocabulary
29© ASM International
SMDDP
Looking toward the future
Develop and employ data management best practices
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In-process data for modeling
Objective: Define how companies and organizations can work together to
expand availability, improve access, and reduce the cost of obtaining
pedigreed data for modeling manufacturing processes and their effects on
material properties and performance.
Outcomes:
• Identified and ranked the types of modeling data that are most
challenging for organizations to obtain.
• Developed options to facilitate collaboration whereby participants
can obtain and share in-process materials data for modeling.
• Published workshop report (available at cmdnetwork.org)
• Refining options for collaboration framework
In-Process Materials Data for Modeling WorkshopAugust 11, 2015; Dayton, Ohio
31© ASM International
Open AM database
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NIU-MSAM project
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NIST Materials Data Curation System
• Written in python• Backed by MongoDB• SPARQL Query interface• XML-based Schema• Table input
Features:• Ability to store templates• Schema management tools• REST API interface • Schema ComposerSource: Robert Hanisch, NIST
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http://rd-alliance.org/
Source: Robert Hanisch, NIST
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Modeling use case
Source: Greg Olson, Northwestern University
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Precipitate growth experiment
• Obtain baseline microstructural data on a sample of 6061-T651• Record data related to constituent particles, grain structure, and texture • Acquire TEM images of precipitate phases to correlate precipitate microstructure
with tensile properties
• Processing details for over-aging Al samples• Expose 6061-T651 samples to four different elevated temperatures:
i. 2 hours at 400Fii. 2 hours at 525Fiii. 2 hours at 650Fiv. 2 hours at 775F (comparable to annealed –O temper)
• Quantify amount of Mg2Si phase after each treatment• Measure mechanical properties at each condition
Source: Warren Hunt, Nexight Group
37© ASM International
Raw microstructure data
Source: Surya Kalidindi, GaTech; Yaakov Idell, NIST
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• Alloy composition
• Temperature as a function of time
• Thermodynamic data (Gibbs energy functions)
• Kinetic data (diffusion mobilities)
• Interfacial energy
• Dislocation density
• Grain size
• Microstructure information related to nucleation sites
Additional data required
Source: Carelyn Campbell, NIST
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Microstructure data
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In summary
• Created a repository and database for Al 6061 data – mechanical,
diffusion, phase, and microstructure data
• Created and refined data schema, metadata, citation, and licensing
protocols
• Developed and tested data importers and exporters
• Developed and conducted a series of heat treatments to collect data to
analyze process-structure-property relationships
• Gathered additional microstructure data to compare with precipitation
simulations and begin modeling the effect of heat on tensile strength
• Looking for partners and collaborators to share the data and explore
additional uses and application ideas
41© ASM International
42© ASM International
Thank you!