Materials Informatics for the ICME CyberInfrastructure

Kim F. FerrisPacific Northwest National Laboratory, Richland, WA

Dumont M. JonesProximate Technologies, Columbus, OH

May 12, 2011

Project ID: LM038

This presentation does not contain any proprietary, confidential, or otherwise restricted information

Project Overview

Budget

Total project fundingDOE – $350K

FY09 Funding - $60KFY10 Funding - $170KFY11 Funding - ~$120K

Start: Mid-Aug 2009Finish: October 2011~2/3 complete (Feb 2011)

Project Timeline Greater materials diversity / joining dissimiliarmaterialsInformation Gaps in ICME Cyberinfrastructure

Data sufficiencyIncomplete structure among partnersCompleteness and availability of repositories

Mg Processing Model DevelopmentFirst principles to Processing - multiscaleMg-sheet forming

Technology Gaps/Barriers

PartnersFord Motor Company (John Allison)Mississippi State University (Mark Horstemeyer, Tomasz Haupt)Canada Center for Mineral & Energy Technology (CANMET) (Kevin Boyle)University of Virginia (Sean Agnew)Metals Bank – Korea (YM Rhyim)ESPEI Workgroup (Liu, Wolverton, Haupt)Joining Materials Knowledge (Beckham)

Project Overview: Project’s place in the cyberinfrastructure

CurrentPrograms

This Informatics Effort

Analysis DB• Fills similar role to data warehouse • Informatics storage/retrieval focused•Analytic platform, not a re-formed repository

record_<prototype>

PK record_id

FK4 sys_idFK3 source_idFK1 var1_id val1 val1_min val1_maxFK2 [pvar1_id] [pval1] [pval1_min] [pval1_max] ...

<var>

record_idsys_idvar1_idval1val1_minval1_max[pvar1_id][pval1][pval1_min][pval1_max]...

var

PK id

name description cardinality prototype unit

source

PK id

name description ...

rule

FK1 origin_var_idFK2 destination_var_id rule

One for each variable

prototype

system

id creation_time

One for each

variable

Repository• Federated/consolidated• Data exchange support• ISO 10303 STEP support

1

Informatics repository augmentation• Property correlations• ID feature space for complex data such as processing sequences• When can data from distinct systems be safely fused• Coverage and lineage anomalies• Insufficient information for prediction (predictor diversity)• Property definition inconsistencies• Material definition inconsistencies

3

2

Overview: Basic Tasks & Motivation

Atomic Mesoscale Macroscopic

AlgorithmInputs

PhysicalInputs

AlgorithmInputs

AlgorithmInputs

2) How can the ‘model’ as a whole be verified; reliable data for verification3) Data from different sources needed for greater variety of materials and joining

1) As parameters are passed from scale to scale, do models adequately represent each scale and what information is lost?

4) Does data support design?

Project Objectives

Challenges:• Greater diversity of materials in design problems• Reliable information needed for new materials and model

development, and verification• Mg-alloy performance properties can conflict. Use data-driven

design/informatics to address this challenge?

Objectives:• Develop materials informatics techniques for robust model

development• Identify gaps in the fundamental information contained in the

ICME knowledge infrastructure• Augment knowledge –address gaps/barriers to developing Mg

alloys• Collaborate with current repositories on knowledge content

Milestones / Deliverables

Month/Year Milestone / Deliverables

Program start.

Milestone: Assess data support needs within context of workgroup research.Deliverable: Preliminary toolkit and data-storage design, assessment of data support. Milestone: Toolkit for informatics data assessment Preliminary Mg-alloy data assessment *

Deliverables: Create and deliver toolkit for lightweight alloys. Validated tools against prototype alloy design data .

Milestone: Extend work to multiple repositories

Deliverable: Apply toolkit to address global data sufficiency.

Feb 2010

Aug 2009

Sep 2010

Jun 2011

Technical Approach

Task 1Informatics Toolkit Design

Contextual assessment of data support for workgroup models development Identify algorithms and storage approach for materials knowledge retention and

generation

Task 2Informatics Toolkit Development

informatics toolkit for data sufficiency/completeness, support for model development, and knowledge augmentation

Analytic support for cyberInfrastructure

Assessment of data support for model development Contextual assessment of data support compatible with heterogeneous sources Collaborative contact with data sources (MSSU, CanMet, MetalsBank, DataSoc)

Task 3

Accomplishments & Impacts

Guidance to First Principles and Cyberinfrastructure Workgroups for data

completeness

Assessed data support for first principles development and materials repository

Data sufficiency – informatics tools to assess modeling power of finite data sets

Data analytics for first principles and sheet forming groups for robust models

Tools to assess repository support for information-driven design

Knowledge content of multiple information sources• Repository/repository• PI/repository• PI/PI

Guidance on integrating ICME applications with repositories and

cyberinfrastructure

Design of prototype data schema Knowledge retention model for

workgroups

Accomplishment #1: Analysis Database Schema

record_<prototype>

PK record_id

FK4 sys_idFK3 source_idFK1 var1_id val1 val1_min val1_maxFK2 [pvar1_id] [pval1] [pval1_min] [pval1_max] ...

<var>

record_idsys_idvar1_idval1val1_minval1_max[pvar1_id][pval1][pval1_min][pval1_max]...

var

PK id

name description cardinality prototype unit

source

PK id

name description ...

rule

FK1 origin_var_idFK2 destination_var_id rule

One for each variable

prototype

system

id creation_time

One for each

variable

•Developed prototype schema as example for knowledge retention• Identify key weaknesses in conversion from conventional storage•Strongly-typed records•Formed design constraints for information transport

Ferris, KF, and DM Jones. 2010. “Assessing Data in Support of Structure-Processing-Property Relationships in Mg-alloy Design” TMS 2010.Jones, DM and KF Ferris. 2010. “Generic Materials Property Data Storage and Retrieval for Alloy Material Applications,” TMS 2010

Technical Accomplishment 2: Enabling the Phase Diagram Infrastructure to MSSU Repository

First principles workgroup: Bridging first principles calculation to phase diagrams

PNNL Contribution• Informatic effort provided Query/Response ‘glue’ for ESPEI, MSSU

Cyberinfrastructure, first principles, Calphad.• Automated procedure, retaining critical information for data• ‘Glue’ formalism applicable to other information joining situations

DBSQL

DBMS-SQL

ESPEI(Materials

Informatics)MSSU DataRepository

Query

Response

CalphadThermoCalc

Technical Accomplishment 3: Tools to Assess Data Completeness

Data Completeness System Definition

Property Definition & Scale Property Correlation

Structure

Den

sity

Are properties ambiguously defined? Should they be resolved by scale or other parameters?

Here, a well-defined system has different density measurements, depending on scale. • These must be clearly distinguished and parameterized in the database• Different end-properties may require one, or both measurements.

Small scale

Small scale

Large scale

Structure

Prop

ertie

s

Are the existing data complete for The desired design parameters?

OK…

More data are required, extrapolation is too great

Existing system

To-be-created system

Structure

Den

sity

Do systems have incomplete structure definitions, so one structure gives more than one property result?

Density models will be ambiguous!

Structure

Prop

ertie

s

Are some properties correlated, so informatics can proceed without complete measures of all properties?

Correlated for a class of structures

Not correlated

Not correlated

Are the existing data complete for the desired design parameters?

Data alone do not guarantee successful information-driven design.

Example: Predicting mechanical constants from individual data points•Harmonic behavior• Two different phases w/ different

responses

Knowing there are two different groupings is critical to correct predictions/models/behaviors

Not having enough information about this data may lead to –• Linear fit• Quadratic model for average behavior• Averaging non-equivalent system (two

phases)•Greater property value uncertainties;

validation/verification difficulties

Ene

rgy

Displacement

Step-through animation sequence illustrates challenge

Incomplete System Definition Can Lead to Incorrect Conclusions

Major property deviations for system definition = alloy+processing+post treatment+shape

AM

50:F

:Die

Cas

ting:

No

Form

AZ3

1:F:

Cas

ting:

No

Form

AZ3

1:F:

Col

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xtru

sion

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or B

ar

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Hot

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old

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et o

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24:H

ot R

ollin

g:P

late

AZ6

1:F:

Hot

Rol

ling:

Pla

te

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1:F:

Cas

ting:

No

Form

AZ9

1:F:

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d E

xtru

sion

:Pla

te

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1:F:

Hot

Ext

rusi

on:R

od o

r Bar

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Hot

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ling:

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te

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EC

AP

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Form

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ting:

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1D:F

:Die

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ting:

No

Form

ZA84

:F:C

astin

g:P

late

Average_grain_size:

Brinell_Hardness_HB:

Tensile_Elongation:

Tensile_Yield_Strength:

Ultimate_Tensile_Strength:

weight_percent:Al

weight_percent:Cu

weight_percent:Mg

weight_percent:Mn

weight_percent:Sb

weight_percent:Sr

weight_percent:Zn

0

10

20

30

40

50

60

70

80

90

100

Property Value Deviations Resulting From Incomplete System Definition (Metals Bank)

• Illustration: % Deviation in Property Values for ‘Nominally’ Equivalent Systems• Problem: Improperly joined information not useful for verification and validation

% DevIn ValueProperty

Systems

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1Estimated impact of system definition components on property errors

compon

ent val

ue (~fr

actiona

l standa

rd devia

tion)

sys po

sttreatm

entsys

proces

sing

sys sha

pe

Averag

e grain

size

Bearing

Streng

th

Bearing

Yield S

trength

Brinell H

ardnes

s HB

Casting

Temp

erature

Compre

ssive E

lastic M

odulus

Compre

ssive S

trength

Compre

ssive Y

ield Str

ength

Corros

ion Ra

te

Creep

Strengt

h

High C

ycle Fa

tigue A

xial

Impact

Toughn

ess cha

rpy

Latent

Heat o

f Fusion

Recryst

allizatio

n Tem

peratu

re

Rotatin

g Bend

ing No

tched

Rotatin

g Bend

ing Un

notche

d

Shear S

trength

Specific

Heat

Tensile

Elonga

tion

Tensile

Yield S

trength

Tension

Elastic

Modul

us

Therma

l Condu

ctivity

Ultima

te Tens

ile Stren

gth

sys allo

y

PCA 1 - alloyPCA 2 - posttreatmentPCA 3 - processingPCA 4 - shape

Tools for data assessment (Metals Bank): Contribution of system definition components to property error

14

Create reduced system definitions andtheir associated error for each

measured property by aggregation

Principal components analysis, usingindicator variables to describe the reduced

System definition components

Normalize components to unitindicator variable height for comparison;

flag non-varying components

Principle: the importance of a system definition component relates to the observed increase in error when that component is removed.

Example: for density, observed error using alloy+shape as a system definition is nearly the same as that for alloy alone.

Shape is not a crucial system definition component for density.

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1Estimated impact of system definition components on property errors

com

pone

nt v

alue

(~fra

ctio

nal s

tand

ard

devi

atio

n)

sys

post

treat

men

tsy

s pr

oces

sing

sys

shap

e

Aver

age

grai

n si

ze

Bear

ing

Stre

ngth

Bear

ing

Yiel

d St

reng

th

Brin

ell H

ardn

ess

HB

Cas

ting

Tem

pera

ture

Com

pres

sive

Ela

stic

Mod

ulus

Com

pres

sive

Stre

ngth

Com

pres

sive

Yie

ld S

treng

th

Cor

rosi

on R

ate

Cre

ep S

treng

th

Hig

h C

ycle

Fat

igue

Axi

al

Impa

ct T

ough

ness

cha

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Late

nt H

eat o

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Rec

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ratu

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atin

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atin

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nnot

ched

Shea

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Spec

ific

Hea

t

Tens

ile E

long

atio

n

Tens

ile Y

ield

Stre

ngth

Tens

ion

Elas

tic M

odul

us

Ther

mal

Con

duct

ivity

Ulti

mat

e Te

nsile

Stre

ngth

sys

allo

y

PCA 1 - alloyPCA 2 - posttreatmentPCA 3 - processingPCA 4 - shape

15

• Two important signatures: “thermodynamic,” “constrained”•Error for core mechanical properties depend on all components

“Constrained” “Constrained”“Equilibrium”

Non-varying components

Tools to identify system definition components controlling property value deviations

Basic data support: Tools for data density...what properties, alloys measured, what aren’t.

Measurement density for Alloys

AM10

0AAM

50AM

50A

AM60

AM60

AAM

60B

AS41

AAS

41B

AZ3

1AZ

31B

AZ61

AZ61

AAZ

63A

AZ80

AAZ

81AZ

81-2

RE

AZ81

-3R

EAZ

81-4

RE

AZ81

AAZ

81-R

EA

Z91

AZ91

-0.2

SbAZ

91-0

.5Sb

AZ91

AAZ

91B

AZ91

CA

Z91D

AZ91

EAZ

92A

EZ33

AK1

AM

1AQ

E22A

WE4

3AW

E54A

ZA84

ZC63

AZE

41A

ZE63

AZK

40A

ZK51

AZK

60ZK

60-0

.5Yb

ZK60

-0.5

YbZK

60-1

YbZK

60-2

YbZK

60A

Aging_TemperatureAnnealing_Temperature

Average_grain_sizeBearing_Strength

Bearing_Yield_StrengthBrinell_Hardness_HBCasting_Temperature

ent_of_Linear_Thermal_ExpansionCompressive_Elastic_Modulus

Compressive_StrengthCompressive_Yield_Strength

Corrosion_PotentialCorrosion_RateCreep_Strength

DensityElectrical_Resistivity

High_Cycle_Fatigue_AxialHigh_Cycle_Fatigue_SN_Curve

Ignition_TemperatureImpact_Toughness_charpy

Incipient_Melting_TemperatureLatent_Heat_of_FusionLiquidus_Temperature

Plate_Bending_NotchedPlate_Bending_Unnotched

Poisson's_RatioRecrystallization_Temperature

Rockw ell_Hardness_HRERotating_Bending_Notched

Rotating_Bending_UnnotchedShear_Elastic_Modulus

Shear_StrengthSolid_Solution_Temperature

Solidus_TemperatureSpecif ic_Heat

Tensile_ElongationTensile_Yield_Strength

Tension_Elastic_ModulusThermal_Conductivity

Thermal_FatigueUltimate_Tensile_Strength

Vickers_Hardness_HVw eight_percent:Agw eight_percent:Alw eight_percent:Caw eight_percent:Cuw eight_percent:Few eight_percent:Gaw eight_percent:Mgw eight_percent:Mnw eight_percent:Ndw eight_percent:Ni

w eight_percent:Otherw eight_percent:Sbw eight_percent:Siw eight_percent:Srw eight_percent:Y

w eight_percent:Ybw eight_percent:Znw eight_percent:Zr

5

10

15

20

25

30

• Data density map for lightweight alloy section of Metals Bank• Illustrates dense and sparse data regions; information gaps• Caveat: Data by itself does not necessarily construe knowledge

Property

Composition

Number ofReported Values

Tools for data assessment: Data coverage summary (Metals Bank/ASM)

17 AZ9

1[A-

C,E

]

AZ3

1

AZ9

1

AZ9

1D

AZ3

1B

AZ6

1*

AZ8

1*

AM

*

AS

*

AZ9

2*

Oth

er (1

8)

Elongation

Strength

Equilibrium

Corrosion

Processing/Tempering

Shape

Hardness

MB ASM

HighModerate

Light

Summary of Technical Accomplishments

Assessed data support in current data resources and current state of data capabilities (Milestone Feb 2010) Analysis repository schema and tested implementation. (Milestone Feb 2010)

Design helps resolve incomplete system or property definitionsAllows direct entry of "as is" data in its original formAccommodates differing system and property definitions

Translated knowledge retention schema into information transfer method for first principles workgroup (ThermoCalc-ESPEI-MSSU-First Principles)Repository assessment tools; assess support for information-driven validation, verification and design (Milestones Sep 2010).

Diagram database content; compare data population (single & combined) Diagram of property value deviations as a function of system; identify system/property combinations with ambiguous property valuesMap and diagram impact of system definition components against property deviationsSystematically look for associations between properties and processing variables

Collaborations

Partners/CollaboratorsMississippi State University (materials repository)Pennsylvania State University (ESPEI integration with repository)ThermoCalc (ESPEI integration, Calphad model)Materials Atlas (Iowa State)CANMET (Kevin Boyle)Sean Agnew (University of Virginia)MetalsBank (materials repository)Discussion in progress for collaborations with other repository developers (ASM)

Technology TransferTechniques/design recommendations for software/repository integration (Pennsylvania State University collaboration)Informatics tools / database design available to ICME communitySharepoint site: https://spteams1.pnl.gov/sites/mat_informatics/default.aspx

Future Work

FY2011Informatics support for multiple data resources•Repository+Repository, Individual+Repository, Individual+Individual•Display ‘global’ information gaps – multi-resource•Physics-based data merging•‘Multi-repository’ additions to system definitions

Materials informatics Toolkit•Data completeness and support•Multiphysics deconvolution for knowledge/system definition augmentation•Modeling power visualization for workgroup validation and verification tasks

Follow-on ProposalFeature development to identify new or missing multiphysicsBridge new compositions to enable more robust designGranularity and multiscale hierarchy in data resourcesRobust strategies to identify/address global information gaps

Technical Back-Up Slides (limit of 5)

Technical Approach – Data Completeness

Identify and fill gaps in ICME knowledge infrastructure required for Mg/Alloy design

Identify data ambiguities -- “same” measurement, different value (missing context in repository).Identify data discrepancies between current repositoriesAssure data are sufficiently complete to engage Phase II modeling – structure-processing/property relations for alloys

Method: materials informatics techniquesLook for structure or processing data correlated with final properties

Examine impact of associating data across multiple length scalesExamine whether data forms must be updated to enable modeling

Anomaly detection Property values that are not defined by specified structure-processing informationAmbiguities across repositories

Collaborate with current repositories on inputs and outcomes

Technical Approach - Knowledge Augmentation

What: after identifying data completeness problems, suggest knowledge augmentation in current alloy/processing databases

Make outcomes available for updates to repositories as neededInformatics tools

Why: Current data may be incomplete or ambiguous for modeling purposesAssess and enable overall data support for information-driven designExamine issues with data ambiguitiesKnowledge assessments are measured against a problem (what is to be learned)

How:Look for structure or processing data correlated with final properties

Examine impact of associating data across multiple length scalesExamine whether data forms must be updated to enable modeling

Anomaly detection Property values that are not defined by specified structure-processing informationAmbiguities across repositories

Example: Data completeness and knowledge augmentation in MSSU repository

Goal: Identify compositions and processing conditions for Mg alloys consistent with improved propertiesData resource: MSSU/CAVS Materials Properties repository

Within the repository Available: Approximately 30 Mg alloysAvailable: Stress-strain curves for each alloyNot available: Detailed composition or processing informationNot available: Yield strength numbers deduced from the stress-strain curve

In advance of detailed analysis (knowledge augmentation)Processing sequences should be added to the repository(Bulk) composition should be added to the repositoryA yield stress feature should be added to the stress-strain curves Other features, based on experience and theoryTry for a small set first, assessing potential association with yield stress

More generally, systematically identify missing information, anomalies in characterization, and associations between structure and properties…

Example: Knowledge augmentation; where, how

It is easy to intermingle properties and systems…

Inconsistent or ambiguous properties (name, form) Difficult to compare across systems

Merged systems (e.g. Alnico II)Merged properties (e.g. Form)Augmentation in this case: can rectify ambiguities

Table 1. An example of alloy property data [CRC Handbook 64th edition]

System Name Composition Density (g/cm3) Tensile StrengthKg/m.m

Form

1 Alnico I Fe 37-39; Al 12; Ni 20-22; Co 5

6.9 2.9 Cast, isotropic

2 Alnico II Fe 52.5; Al 12; Ni 24-26; Cu 6; Co 12.5

7.1 2.1 Cast, isotropic

2 Alnico II Fe 52.5; Al 12; Ni 24-26; Cu 6; Co 12.5

7.1 45.7 Sintered, isotropic

3 Alnico III Fe 59-61; Al 12; Ni 24-26; Cu 3

6.9 8.5 Cast, isotropic

4 Alnico IV Fe 55-56 ; Al 12; Ni 27-28; Co 5

7.0 6.3 Cast, isotropic

5 Hypothetical Calculation

Fe 40, Ni 24, Co 5 9.0 60 Repeated fcc unit cell

More Detailed Summary -Toolkit Accomplishments

Repository assessment tools; assess support for information-driven validation, verification and design

Diagram of database contentAbility to survey data population and compare repository contentLocate information gaps; identify/prioritize maximum leverage pointsIdentify data conditions conducive to robust validation of model predictions/experimental measurementsDiagram of property value deviations as a function of systemIdentify repository system/property combinations with ambiguous property values (anamolies/system and measurement uncertainties)Indicate which systems/property combinations are most susceptible to large property variationsMap and diagram impact of system definition components against property deviationsSystematically look for associations between properties and processing variables