C. E. Campbell and U. R. Kattner

Material Measurement Laboratory, Materials Science and Engineering Division

A. Dima, D. Foxvog, P. J. Dessauw, and P. Savoritto

Information Technology Laboratory, Software and Systems Division 

2nd World Congress on Integrated Computational Materials Engineering 

Salt Lake City, Utah

July 9, 2013
NIST Information Technology Laboratory

Information Systems Group

Y.-S. Li-Baboud, B. Long 

NIST Material Measurement Laboratory

Technical Program Director for Materials Genomics

J.A. Warren

Thermodynamics and Kinetics Group

B.P. Burton, R.H. Taylor, C.A. Becker, F. Tavazza, Z. Trautt

Cell Systems Science Group

T.N. Bhat, J.T. Elliott

Kent State University, Center for Materials Informatics

Laura Bartolo
Microstructure 

Prediction Tools

Processing 

Modeling Tools

Materials 

Properties

Prediction Tools

Data 

Informatics & 

Tools 

Designed New 

Material

Material

Performance 

Criteria 

First 

Principles

nist.matdlorg

CALPHAD

nist.matdl.org

DATA

(Experimental and 

Calculated)

Atomistic Simulations

http://www.ctcms.nist.gov/potentials/

Tools: e.g. 

CMS

Reference 

Data

Data Informatics and Tools 

Goal: decrease the cost and time-to-market by 50%
Materials Are Complicated Hierarchical Systems

• Advanced materials often consist of several components

(generally, n > 5) and multiple phases. 

• The material properties are dependent on the microstructure. 

• The microstructures changes as a function of processing and 

service conditions. 

Material A at Temp 1 

Material A at Temp. 2

4

Performance

Key to material design: 

• What phases are present

• Composition and morphology of 

the phases present

 Composition & phase dependent 

property data (CALPHAD)
Melting Temperatures 

Critical Temperatures

(Phase Changes) 

Lattice Parameters

Tracer

Diffusivities

Phase fractions and 

compositions

Heat of Formations

Activation energies

 Phase-based property data: Thermodynamic quantities, diffusion mobilities, 

molar volume, elastic properties, thermal conductivity, etc. 

 Emphasis on binary and ternary data to predict multicomponent properties

 Data can be experimental or computational. 

0

0.02

0.04

0.06

0.08

0.1

0.12

-1000 -500 0 500 1000

Mass Fraction

Distance (μm)

Cr

Co

W

Ta

Al

Ti

Mo

Re

Nb Hf

Composition 

Profiles

Enthalpies of mixing

Heat Capacities

Crystal structures

Micrographs/Morphologies

3-D Atom probe Tomography
Data 

• Elements present

• Type of value (e.g. enthalpy, heat of formation, phase boundary, diffusivity, lattice parameter, bulk moduli)

• Experimental or computational method

• Type of measurement (direct or indirect)

• Number of phases present

• Datum value and error

• Type (single value or series)

• Units

• Actual value(s) and error(s)

• For each phase present

• Phase name

• Composition and fraction and errors

• Crystal structure (this input will follow the format prescribed by the CCN) or amorphous 

• Lattice parameter 

• Temperature and error

• Pressure and error

• Type of Material

• Bulk composition

• Material purity

• Sample preparation

• Microstructure information 

• Single crystal

• Polycrystalline (grain size, dislocation density)

• Non-crystalline 

Metadata

• Data manipulation details (if any, e.g. reference state corrections, analysis method to determine interdiffusion coefficient) 

• Reporting format (raw data, digitized data, other) 

• Reference (DOI or text ; one must be present)

• Additional information
Phase-Based Property Database

 Material Property Database Exist 

Generally, focused on engineering/design specs or first-principle 

calculations results. 

• Focus on phase-based properties that are needed to 

describe the composition, temperature, and 

pressure functions of a phase.

• Unary, binary and ternary data are primary focus.

• Multicomponent data are needed for validation 

Materials Universe 
Industry user just wants 

to the know the 

diffusion coefficient at 

given temperature for a 

given material.

D = 2.16e-15 m2/s

D= D

0 exp

(-Q/RT)

Calphad user 

who wants to 

complete a new 

assessment 

Diffusion Coefficients

Diffusion Couple 

Composition Profiles

Composition Profiles

Raw Data (i.e Intensity vs

Distance)

Advanced 

expert wants to 

understand the 

diffusion 

mechanism 

DATA
Schemas Ontology Data

Documents

Middleware

Web Interface

Computational

Resources

Future 

Data

Workflow

Engine
Ontologies

XML Schemas

Low-Level 

Domain 

Language

Transform

XSLT

Reasoners

Extract CALPHAD

Data

Other Formats

MatML RDBMS

Tool

Formats

Thermo

ML Lab Equipment

Computation

Text files

Spreadsheets

Diagrams

Articles

Databases

Materials Ontology currently being developed

Note this is a work in progress

Tool Chain

Section of XML Schema

NoSQL

Databases

MongoDB, Neo4J
Broad concepts 

covered in materials 

data files (data have 

many types)

– Objects, Materials, 

and Events

– Physical Properties

– Documents

– Data Objects & 

Types

– People & 

Organizations

– Software

– Relations among 

these

An ontology renders shared vocabulary and 

taxonomy which models a domain with the 

definition of objects and/or concepts and their 

properties and relations.
• Semantic Unification

– The unification of lexically different representations 

that have the same semantics

• Example: fcc phase in steels can be referred to as fcc, 

austenite or γ.

• Ontology-based Data Integration

– Using ontologies to unify data that share some 

common semantics but originate from unrelated 

sources

• Example: Are property data from two experiments consistent 

enough to be combined?

• Ontologies are not static and can grow with needs
• Why use a graph 

database?

– True networked 

database with queries, 

ACID, and REST interface

– All apps can share the 

same representation

– Overcomes some of the 

limitations of RDF

– Flexible visualization...
Sources

• Prototype MGI Ontology

• ThermoML

• MatML

• MatSeek

• UnitsML

• ChemML

Tools

• UML (Unified Modeling Language)

• Semantic Web (RDF, OWL)

Note: This is a generalized model 

depicting overall structure
Core CALPHAD Tabular Data (HDF5)

Measurement Units (Units ML)

• Why XML?

– Internationalization

– Flexibility

– Transformability

– Interoperability

– Longevity

– Web-Enabled

– Available Resources
• MongoDB

– Schema-less, cloud-friendly

– High Performance, scalable

– Used by CERN enable 

information discovery on 

Compact Muon

Solenoid data

• Reason: “dynamic 

queries, full indexes, 

including inner objects 

and embedded arrays, as 

well as auto-sharing”

XML→BSON

MongoDB

BSON→XML

XML
• Need for reference diffusion data for the pure elements

• Current process:

Data

Search data

Digitize Data 

Plot data Reference values

-32

-31

-30

-29

-28

-27

-26

-25

-24

5 5.5 6 6.5 7 7.5 8 8.5 9

Murdock (1964)

DeReca and Libanati (1986)

Kohler (1987)

Pontau (1979)

Ghosh assessment (2002)

ln D Neuman

Ln D*

Ti 

 m2/s

1/T (K) x104
Future Data Informatics 

XML Schema

Various Database Platforms 

JSON

Data Tools:

Statistics; Machine 

Learning
• Building a data infrastructure that enables 

complex searches and allows for data mining 

and machine learning

 Need methods to facilitate data capture

• User defined templates

• Automated capture from instruments 

• Electronic laboratory notebooks?

 Community participation 

• Incentives from journals and funding sources