Information sciences to fuel the data age of materials science

Information Sciences to Fuel the Data Age of Materials Science

Tony Fast Materials Data Analyst

MINED Materials Informatics for Engineering Design

Georgia Institute of Technology

MATERIALS SCIENCE IS BIG DATA

Ad Hoc Standards, Silos, Integration, Software, Equipment, Data Formats, Ideas

The Materials Innovation Network is a collaborative environment built to fuel the Materials Genome Initiative by managing users and their digital data for microstructure driven materials development and improvement. SOCIAL NETWORK Manages users, projects, and expert communities engaged in materials science related efforts. A melting pot for materials scientists, big data, and integration. CODE REPOSITORY A platform with an embedded versioning system to develop codes and deployable tools for the MATIN community at large. This platform will enable good coding practices and rapid delivery of academic utilities to market quickly. DATABASE The database is the unifying feature of MATIN. This graph database is specifically designed to store nearly all types of materials datasets, maintain data provenance, semantically query metadata, learn design patterns ( or workflows ), support the big data generation, and establish a federated database with access control for academia, industry, and national labs.

Database

Information

Models Analytics

Codes Users

Social Network Versioning Control Database API

Collaborative Workspace

TEAM 1

Information / Code Upload

Query/Download

Analytics

Model Development Dat

a P

rove

nanc

e New Data

Object

Workflow

Data Object

Metadata

Collaborative Workspace

TEAM 2

Information / Code Upload

Query/Download

Analytics

Model Development

Metadata

MATIN will enable • Collaboration • Standarization • Electronic

Recording • Data Management

and Federated • High Value Testing • Knowledge

Transfer

STRUCTURE INFORMATICS WORKFLOW

PHYSICS BASED MODELS SIMULATION EXPERIMENT

MICROSTRUCTURE (MATERIAL) SIGNAL MODULES

ADVANCED & OBJECTIVE STATISTICAL MODULES

DATA MINING MODULES

VALUE ASSESSMENT

INTE

LLIG

EN

T D

ES

IGN

OF

EX

PE

RIM

EN

TS Microstructure Informatics is a data-

driven system to mine structure-property/processing connections from experimental and simulation materials science information. The system is agnostic to material system and length scale, objectively quantifiable, and rapidly iterates in less cycles for both materials improvement and discovery.

Microstructure signal modules are (semi) automated tools to identify local and effective microstructure features.

Aluminum in Epoxy Titanium

EMMPM - BlueQuartz

Bamboo

Martensitic Steel SiC/SiC Al-Cu Solidification

ADVANCED & OBJECTIVE STATISTICAL MODULES

THE MICROSTRUCTURE IS A SAMPLE IN AN IMMENSE STATISTICAL POPULATION.

α-β Titanium

SPATIAL STATISTICS

fthh ' =

mshms+t

h '

Dts∑t t

t

Statistical correlations between random points in space/time which reveal systematic patterns in the microstructure. Contains the original µS within a translation & inversion.

CURRENT APPLICATIONS metals, polymers, fuel cells, cmc, md, & a bunch of other things

TYPES OF SIGNALS sparse, experimental, simulation, heterogeneous, surface, bulk

DATA MINING MODULES

Microstructure Material Processing Property

Mining modules are machine Learning solutions to extract rich bi-directional structure-property/processing linkages from materials & microstructure datasets. Mining modules create structure taxonomies, homogenization and localization relationships, ground truth comparison between simulation and experiment, materials discovery, and materials improvement.

Objective Microstructure Classification of α-β Titanium Images àStatisticsààMine with Principal Component Analysis

Mechanical Deformation of Polymer Chains

Molecular Dynamics of Aluminum Atoms

MPL

GDL

X-CTàFinite Element ModelingàStatisticsàà Regression to connect the statistics with diffusivity values from FEM

Bottom-up Homogenization Relationships

FEM"ε=5e-4"

Meta-modeling with Materials Knowledge Systems Top-down localization relationships

ps = athms+t

h

h∑

t∑

The MKS design filters that capture the effect of the local arrangement of the microstructure on the response. The filters are learned from physics based models and can only be as accurate as the model never better.

OTHER APPLICATIONS"Spinodal Decomposition, Grain Coarsening, Thermomechanical, Polycrystalline

Top-Down Localization Relationships for High Contrast Composites

The MKS is a scalable, parallel meta-model that learns from physics based models to enable rapid simulation at a cost in accuracy.

N2 vs. Nlog(N) complexity

It learns top-down localization relationships to extra extreme value events and enables multiscale integration.

Structure-Processing MKS Processing History

Structure-Property Homogenization

Structure-Property Localization

Illustrating Integration

Data enables bidirectional S-P/P, multiscale integration, and higher throughput

OTHER DOMAINS THAT WILL FUEL BIG DATA IN MATERIALS SCIENCE information gain theory, digital signal processing, regressions, statistics,

high performance computing, cloud computing, databases, mobile devices, a connected community