Materials Genome Initiative: Implications for University Education 

and Research

David L. McDowell1,2Co‐Director, NSF Center for Computational Materials Design

1School of Materials Science & Engineering2Woodruff School of Mechanical Engineering

Georgia Institute of Technology, Atlanta, GA 30332

March 29, 2012

Materials by DesignOlson, Questek…

Integrated Computational Materials EngineeringNAE NMAB

Materials Genome Initiative

Cyberdiscovery of Materials ‐ NSF

Accelerated Insertion of Materials ‐ DARPA

The Road to Competitive Advantage

• There is a persistent demand for improved materials to advance emerging technologies

• Current time to introduce new materials is much longer than that of design and prototyping

• Couples with manufacturing competitiveness

Common Themes

Concurrency of materials discovery, design, and product development

Basis of Materials Genome Initiative

From MGI White Paper

MGI

Both demand new science and technology development

Strands of Materials Genome/ICME

Discovery – Facilitated largely by targeted/guided combinatorial first principles and atomistics simulations to discover stable phases with required structure and physical/chemical/electrical properties. Screening methods are at a premium.

Development – Historically by empirical means; improvement is emphasized. Recent trend (last 10-15 years) is to combine simulation and experiments with materials synthesis and processing to accelerate insertion of new and improved materials. Very broad, early stages. Often arrives at metastable phases that are useful in applications.

Selection – Classical design scenario – compromise selection based on properties, e.g., Granta (Ashby) materials selector software. Relatively well-established. The way “designing” materials is currently taught in universities.

Development: Upscaling and Product/Design Integration

• DARPA AIM program (2001‐2004) –Accelerating the insertion of new and/or improved materials• D3D tools (ONR/DARPA, 2005‐2009)• Integrated Computational Materials Engineering (2007‐present)

Accelerating New Materials Discovery

• NSF Cyberinfrastructure (2003) and DMR Cyberdiscovery of Materials (2006)• DOE Computational Materials Science & Chemistry: Accelerating Discovery and Innovation through SBES ‐ 2010

Materials Genome Initiative ‐ 2011

Two Complementary “Strands”

ICME strandDiscovery strand

Designer Knowledge Base: Integration

DARPA AIM: McDowell, D.L. and Backman, D., “Simulation‐Assisted Design and Accelerated Insertion of Materials,” Ch. 19 in Computational Methods for Microstructure‐Property Relationships, Eds. S. Ghosh and D. Dimiduk, Springer, 2010, ISBN 978‐1‐4419‐0642‐7.

Dr. L. ChristodoulouNi‐base superalloys for aircraft gas turbine engines

PWAGE

Some Comments: Aerospace Alloys

• Microstructures that give rise to desired properties are composed of multiple multicomponent phases with interfaces.

• Structures are typically achieved through path-dependent, non-equilibrium processes.

• Interface effects on phase interactions are a weak point in modeling and simulation – necessitates multiscale modeling in many cases.

• Processing induces residual stresses, composition gradients, porosity, inclusions, segregation and coarsening, etc. that have strong effects.

Changing the Focus from Properties to Structure

processing properties & responses

Microstructure (Genome) ‐ NEW

Properties (Materials Selection) ‐ OLDStructural Materials

Structure

Properties

Performance

Goals/means (inductive)

Cause and effect (deductive)

Processing

Structure

Properties

Performance

Goals/means (inductive)

Cause and effect (deductive)

Processing

8Olson, Science, 1997

Shift to Concurrent Product-Process-Material System Design

SystemSubsystemsComponents

PartsMaterials

SystemSpecifications

MesoMacro

MolecularQuantum

MaterialSpecifications

Match the time frame

CCMD (NSF I/UCRC)Zi‐Kui Liu, DirectorD. McDowell, Co‐Director

Multiscale Modeling and Multilevel Design

Structure

Properties

Performance

Goals/means (inductive)

Cause and effect (deductive)

Processing

Structure

Properties

Performance

Goals/means (inductive)

Cause and effect (deductive)

Processing

10

Decision-Making in Materials Design

• Accelerated discovery, development and insertion of materials is a decision-making endeavor

• The aim is to increase the fraction of critical decisions informed by modeling and simulation

• Materials design/development is typically undertaken in the context of product development/enhancement – coupling with manufacturing is natural

• Computationally-assisted materials design and development involves decision support

• Decision support concerns reliability, probability, multi-objective optimization, uncertainty quantification and management

• A systems perspective is necessary

Expanded Elements of Materials Innovation Infrastructure

Digital Data

Computational Tools

Experimental Tools

Hierarchical and concurrent multiscale models* process‐structure* structure‐property

Materials discovery ‐ first principles & atomistics

Decision‐based multiobjective systems design

• Design exploration• Detail design

Unit process models for manufacturing

Designer Materials Knowledge System

Verification and Validation ‐Experiment/Model coupling

Distributed collaborative networks

DatabasesSensors and in situ measurements

UQ and uncertainty management

Materials characterization and microstructure representation

12

Data sciences and material informatics

Coupling Modeling and Simulation with Experiments at Critical Length and Time Scales   4D, space and time• Behavior of interfaces• Scale effects (length and time)• Competing mechanisms• Estimating parameters and coarse‐grain model forms in 

multiscale model chains

Materials Characterization for a Knowledge‐Based Approach• High‐throughput 4D characterization protocols.• Quantifying resolution, accuracy and uncertainty in the 

measurement datasets .• Schema to balance of investment in characterization and for 

targeting specific microstructures.

Coupling Modeling and Simulation with Experiments at Critical Length and Time Scales   4D, space and time• Behavior of interfaces• Scale effects (length and time)• Competing mechanisms• Estimating parameters and coarse‐grain model forms in 

multiscale model chains

Materials Characterization for a Knowledge‐Based Approach• High‐throughput 4D characterization protocols.• Quantifying resolution, accuracy and uncertainty in the 

measurement datasets .• Schema to balance of investment in characterization and for 

targeting specific microstructures.

Critical Technology Gaps Limiting ICME/MGI: Development Stream

(Dr. J. Christodoulou)

From G.B. Olson

Computational Modeling and Simulation; Materials Theory• Protocols for linking unit process models that consider 

atomic/molecular structure to higher length and time scale modeling protocols.

• Methods for both homogenization and localization.• Identification of key levels of material hierarchy that control 

target properties/responses and dominant mechanisms to steer investment in experimental effort.

• Schemes for deciding if new information (and cost) is necessary to add commensurate value to decision‐making.

• Balancing relative investment in experiments and model refinement based on utility to inform decisions in materials design and development.

Computational Modeling and Simulation; Materials Theory• Protocols for linking unit process models that consider 

atomic/molecular structure to higher length and time scale modeling protocols.

• Methods for both homogenization and localization.• Identification of key levels of material hierarchy that control 

target properties/responses and dominant mechanisms to steer investment in experimental effort.

• Schemes for deciding if new information (and cost) is necessary to add commensurate value to decision‐making.

• Balancing relative investment in experiments and model refinement based on utility to inform decisions in materials design and development.

Critical Technology Gaps Limiting ICME/MGI: Development Stream

Microstructure – A Hallmark of Modern Structural Materials

Representation and ComputationalStructure-Property Relations ofRandom Media

D.L. McDowell, S. Ghosh, and S.R. Kalidindi

Vol. 63 No. 3 • JOM, 2011

Computing properties and responsesFrom statistical ensembles

Local                                Global

Microstructure-Sensitive Fatigue:Localization Problem

Digital SVEs

Simulations

Experimental Calibration/Validation

Distribution of FIPs

Robust Materials (Morphology) Design

Rank‐Order Range of Microstructures   (for max./min. life or min. 

variability)

Assess Fatigue Variability via FIPs

Przybyla & McDowell2010

17

Computational Microstructure-Sensitive Probabilistic Fatigue Design Framework

2. Identify EV response of SVEs via simulation

3. Characterize EV distributions of key response 

parameters

0.0001 0.001 0.01.01.115

102030507080909599

99.999.99

Simulated Extreme Value FIP

CD

F

Strain=0.5%

Strain=0.7%

4. Characterize correlated microstructure attributes coincident with the EV response (EV marked correlation functions)

1. Generate multiple SVEs based on predefined distributions of key 

microstructure attributes

5. Identify extreme value correlated attributes key to response and rank 

microstructures6(b). Select top candidates for experimental evaluation

1 1, , , n 1 1, , , n

Experimental Calibration/Validation

6(a). Iterate materials design

Groeber et al. 2007, IN100

mg=0.45 to 0.5 (Basal, primary α) 

mg’=0.45 to 0.5 (Basal, primary α)mg=0.45 to 0.5 (Basal, primary α) 

mg’=0.45 to 0.5 (Prismatic, primary α)

mg=0.45 to 0.5 (Basal, primary α) 

mg’=0.45 to 0.5 (Pyramidal <a>, primary α)

mg=0.45 to 0.5 (Basal, primary α) 

mg’=0.45 to 0.5 (Pyramidal <a+c>, primary α)

Extreme Value Marked Correlations in Random Orientation Distribution Ti-6Al-4V

Cluster of similarly oriented equiaxed α for easy basal or 

prismatic slip

Equiaxed α oriented for easy basal or prismatic

Equiaxed α oriented for hard <c+a> slip

Easy slip region

*S. K. Jha,  J. M. Larsen,  VHCF‐4, pp. 385‐396, 2007

19

Extreme Value Fatigue Indicator Parameter Distributions in Duplex Ti-6Al-4V

‐2.00

‐1.00

0.00

1.00

2.00

3.00

4.00

5.00

6.00

0.00E+00 1.00E‐10 2.00E‐10 3.00E‐10 4.00E‐10

ln(1/ln(1/p))

Extreme Value FS FIP

A

B

C

D

20

A Fine bi-modal low α

B Fine bi-modal high α

C Coarse bi-modal low α

D Coarse bi-modal high α

Gumbel Distribution (Type I):

exp n n n

n

y uY nF y e

Material-Specific Scale Considerations

• Nanostructured materials and devices –process-structure-function fully concurrent

• MEMs – some separation of scales and functions

• Aircraft gas turbine engine materials – huge range of scales Tertiary precipitates ~ 10-30 nm Secondary precipitates ~ 50-500 nm Primary precipitates ~1 m Grains ~ 3-30 m Inclusions and Large grains ~100-200 m

Statistical Methods and Uncertainty• Information management for a flexible modeling digital data 

linked environment.• Probabilistic treatment of rare event (extreme value) 

phenomena and treatment of anomalies.• Value of information metrics applied to coupling models with 

decision‐making and models with experiments at critical length and time scales.

• Stochastic methods for uncertainty and V&V algorithms that couple experiments with simulations.

Informatics and Data Science – Beyond Databases & Datamining• Rapid, efficient protocols for estimating properties, 

calibrated/validated with experiments and high fidelity models• Structure as the taxonomy for materials design and 

development

Statistical Methods and Uncertainty• Information management for a flexible modeling digital data 

linked environment.• Probabilistic treatment of rare event (extreme value) 

phenomena and treatment of anomalies.• Value of information metrics applied to coupling models with 

decision‐making and models with experiments at critical length and time scales.

• Stochastic methods for uncertainty and V&V algorithms that couple experiments with simulations.

Informatics and Data Science – Beyond Databases & Datamining• Rapid, efficient protocols for estimating properties, 

calibrated/validated with experiments and high fidelity models• Structure as the taxonomy for materials design and 

development

Critical Technology Gaps Limiting ICME/MGI: Development Stream

Y

XType I, II, IIIRobustSolution

Upper Limit

Lower Limit

ResponseFunction

Deviationat Optimal Solution

Deviationat Type I, II Robust Solution

Deviationat Type I, II, III Robust Solution

DesignVariable

Type I, IIRobustSolution

OptimalSolution

Decision-Making with Uncertainty

• Type I: System variable (noise) uncertainty

• Type II: Design variable uncertainty

• Type III:Model parameter/structure uncertainty

• Multi‐level design: IDEM

H. Choi, 2005.

McDowell, D.L., Panchal, J.H., Choi, H.‐J., Seepersad, C.C., Allen, J.K. and Mistree, F., Integrated Design of Multiscale, Multifunctional Materials and Products, Elsevier, October 2009 (392 pages), ISBN‐13: 978‐1‐85617‐662‐0

23

Workforce Issues

• There is a shortage of human resources to realize MGI goals downstream.

• Students must have a broader systems perspective along with computational materials science and mechanics strengths.

• Just developing tools and methods won’t add enough value to the develop the workforce.

• New curricula and distributed collaboration is necessary that recognizes the interdisciplinary nature of materials design and development.

University Perspective

• Where are the technology gaps? Primary obstacles? They may differ substantially from usual gaps related to multiscale modeling, etc.

• How can we assist with workforce development via educational programs and formation of collaborative teams within the Innovation Infrastructure of the MGI?

Digital Data

Computational Tools

Experimental Tools

Thanks!

McDowell, D.L., “Critical Path Issues in ICME,” Tools, Models, Databases, and Simulation Tools Developed and Needed to Realize the Vision of Integrated Computational Materials Engineering, Symposium held at Materials Science and Technology, 2010, ; ISBN 978‐1‐61503‐726‐1, 2011.

McDowell, D.L. and Olson, G.B., “Concurrent Design of Hierarchical Materials and Structures,” Scientific Modeling and Simulation (CMNS), Vol. 15, No. 1, 2008, p. 207.  

50nm

50m50m

2m

Microvoidingmatrix +primary particles

debonding

shear test

microm

microij

micromicrop

ij

)(microij

micromicrop

ij

)( macro

macro

macro

macromacro

ij

pijE

)(macro

macromacro

ij

pijE

)(

Iron matrix +secondaryparticles

Subatomic scale

Multi-scaleConstitutivelaw

Fracture toughnessFracture Toughness