The MGI and AIJames A Warren

Director, Materials Genome Program

Material Measurement Laboratory

National Institute of Standards and Technology

Executive Secretary, NSTC Subcommittee on MGI

www.mgi.gov

Acknowledgements

Alex

NIST Colleagues including C. Campbell, A. Dima,

Z. Trautt, R. Hanisch, M. Brady, E. Lin, G. Kusne,

M. Green, J. Hattrick-Simpers, E. Lass, B. DeCost

Bryce Meredig (Citrine)

So many others that I’ve overlooked.

Power CorruptsVint Cerf

Outline

Define MGI

Racing towards AI, what does that mean?

Background and Models

Approaches and Successes

Humans + Machines

The Materials Genome InitiativeA Multi-Agency Effort

It’s an InitiativeDOE, NSF, NIST, DOD, NASA, FDA, NIH…

Through the Transition

To decrease time-to-market by 50% while <$$

Develop a Materials

Innovation Infrastructure

Achieve National goals in

energy, security, and human

welfare with advanced

materials

Equip the next generation

materials workforce

Materials Genome Initiative for Global Competitiveness

Span the Continuum

The G in MGI

Metaphors

Data Models

Simulation

Experiment

Quantum MacroMicroNano

Materials w/ Targeted Properties

The MGI Approach

Creating and Capturing Knowledge of Materials

M in MGI

MATERIALS PROPERTIES ARE HISTORY DEPENDENT

SOUFFLE INGREDIENTS

• 2 tbsp (30 mL) butter

• 2 tbsp (30 mL) all-purpose flour

• 1/2 tsp (2.5 mL) salt

• Pinch pepper

• 3/4 cup (175 mL) milk (1%)

• 4 eggs

• 2 egg whites

• 1/4 tsp (1.25 mL) cream of tartar

MGI - MATERIALS RESEARCH

THE CENTRAL PARADIGM OF MATERIALS RESEARCH▸ Materials Research is about making useful materials (transistors, LED,

titanium turbine blades, sporting goods…)

▸ Composition (Ingredients)

▸ Processing (Recipe)

▸ Structure (Souffle or Mess?)

▸ Properties (Fluffy, Delicious vs. Rocklike Charcoal)

▸ Challenge is to design new materials from existing knowledge without resorting to raw “Edisonian” repetition.

A Material’s Properties Depend on its History

Materials Are Complicated SystemsModeling is a Challenge

Alloy cooled from 300 °C

Alloy cooled from 800 °C

The Decade of MGI?These ideas are not new

Milanese Loop Alloy

High Strength 18K Gold

Anodizable 7000 Aluminum

-Custom Magnetic Stainless Steel

-2X harder

-60% stronger Al

-30% lighter than 316L

Apple watch-Announced September 2014

Baseline: 316L Stainless Steel

-Cold-forged to 40% harder

-Special purity mirror finish

Prospective Economic Impact

The potential economic impact of improved

technology infrastructure supporting materials

innovation in the United States is conservatively

estimated to be between $116 billion and $240

billion per year:

mgi.nist.gov/keyReport

MGI IN SUM

• The MGI Is about improving our ability to design and deploy new materials (faster)

• Need better (or just any) data and models

• The MGI is essentially a direct consequence of our improvements in computational power and associated models, coupled to the disruptive consequences of the Internet.

• There are a limited number of ways to get the “knowledge” that is the fuel for the MGI

• High Throughput Computations, w/ published data and software

• High Throughput Experiments

• Get it from everywhere (Mine the literature, mine published data, if only it were published!)—- Change Publication and feed the models

WHAT IS AI (TODAY)

NOT THE COMPUTER SCIENCE

DEFINITION BUT INSTEAD THE

COLLQUIAL ONE

(MACHINE LEARNING, DEEP NEURAL

NETS, ETC)

BUT NOT ROBOT DOOM

Mastering the curse of

dimensionallity

This talk is aging quickly

Growth of Materials Informatics

0

750

1500

2250

2003 2005 2007 2009 2011 2013 2015

Men

tions in P

ape

rs

materials informatics materials genome "machine learning" & "materials science"

White House launches

Materials Genome

Initiative

Citrine InformaticsBryce Meredig

2017 Gartner Hype CycleEmerging Technologies

The Relationship between Models, Measurement, Data, and

PublicationAn old polemic, illustrating some of the key questions, gaps, obstacles to

realizing materials by design

Traditional Approach Example

Measure the modulus of a

steel coupon by a tensile test

(measurement of strain)

record values is some sort of

table (perhaps Excel)

perhaps publish

publication contains

metadata

What the #$&% ̂is a modulus

It’s only a (constitutive) model (not reality)

It’s an equation

What you think you’re measuring is NOT the modulus

You are measuring a strain and assuming the model is

true.

A model is a lie that helps you see the truth. - Howard Skipper

All Models are Wrong, but some are Useful -- George Box

You can’t measure without a model

Measurement without Models is nearly

MEANINGLESS

APS Physics

Oct 13, 2017

BETTER MODELS = LESS DATA

TAKE THE LHC AS AN EXAMPLE

DATA PRODUCED AT 1PB/SEC !

REDUCED DATA SAVED: ~ GB/S

THAT’S A DARN GOOD MODEL

OTHER END : BIOLOGY?

MATERIALS: IN THE MIDDLE

Where does AI fit in this picture?

Some Reflections

Importance of Models in general

AI is a method for creating a model (descriptors and predictions)

AI might be viewed as a method for “coarse graining”

Physics is a model creation machine

Correlation versus causation & Notions of induction and ground truth

THIS IS WHAT MAKES AI+SCIENCE SPECIAL (versus recommendations for

what to buy with your socks)

In other words, we can be DATA POOR (and we often are) because we

(often) have good models

MGI is both necessary to and will be influenced by the coming revolutions in Manufacturing

Digital Thread

Fab Lab

Some MGI ExemplarsBuckle up

Design of Cu-Ni-Zn-(Mn) alloys with tailored electrical conductivity

Eric A. Lass, Mark R. Stoudt, Maureen E. Williams, Carelyn E. CampbellMaterials Science & Engineering Division

National Institute of Standards and TechnologyGaithersburg, MD, USA

Tony YingUnited States Mint

Washington, DC, USA

ICME 2017May 21-25, 2017Ypsilanti, MI, USA

Olson systems design diagram

Secondary Processing

Secondary Processing

Secondary Processing

A new improved alloy

Constructed a Calphad-type model for color, identified the entire composition space in quaternary Cu-Ni-Zn-Mn possessing the correct conductivity, but with varying colors from white to yellow

Identified the “best” (cost effective) alloy meeting the customer’s requirements, and where to move in composition space if color-requirements are relaxed

Exemplars #2 Databasing

DFT base stories

Materials Project

AFLOW

OQMD

NOMAD

MARVEL/MAX

MAST-ML

Extremely Well Curated Data

Chuck Ward

GALILEO EXAMPLE

MICHAEL NIELSEN

ACADEMIC INCENTIVES

THE MOONS OF SATURNGalileo no doubt planned to publish this new

discovery in his next book, but in the meantime, how

could he preserve his priority and prevent others from

claiming the discovery as their own? His solution was

to circulate an anagram, s m a i s m r m i l m e p o e t

a l e u m i b u n e n u g t t a u i r a s. Others would

know that he had discovered something and when he

had discovered it, but they would not known what the

discovery was. The number of letters in the anagram,

37, was too small to allow him later to fudge and

change the solution to describe a discovery made by

someone else in the meantime. Before the days of

scientific papers (invented in the 1660s) this was an

effective (if not always foolproof) method of claiming

priority.

http://galileo.rice.edu/sci/observations/saturn.html

We need to both change the rules and give people tools to make that easier

Why Curate Your Data?

Increase your productivity/efficiency

Can you find it easily

Do you remember what the data means (the experimental/simulations

conditions)?

Improve reproducibility

Improve research continuity (next student/staff member knows where to start)

Increase your research impact; others can find it and re-use it.

Minimize data loss

Adhere to government mandates

ENABLE DATA DRIVEN RESEARCH

Material Measurement Laboratory, Jan 23, 2017, Z. Trautt

Motivated by the Astronomy Community

Value Multiplier

Astronomy vs. Material Measurement

Measurement Type

Po

siti

on

in t

he

sky

Singular data models Modular data models

e.g.

e.g.

Measurement Type

Mat

eria

l syn

thes

is a

nd

p

roce

ssin

g h

isto

ry

48WHAT’S OUR TELESCOPE?

AI Efforts

MML AI Projects

AI Self-QA Using Learning Curves in Feedback Loops

(Congo)

Autonomous Materials Laboratory: Phase Mapping

(Kusne)

Combining High-Throughput Experimental Science

and Machine Learning to Accelerate Materials

Innovation (Hattrick-Simpers)

Deducing Prior Deformation from Simple Mechanical

Analysis (Reid)

High-Performance Crystal Plasticity by ML-driven

Interpolation (Reid)

JARVIS-ML: Machine Learning Prediction of Material

Properties (Choudhary)

Develop and Test Novel Root and Rule-Based

Natural Language Processing (NLP) Tools for

Information Indexing and Searching (Sriram, Bhat)

Large-Scale Atomic Force Microscopy Data Mining

and Analysis (Persson)

Learning Networks for IoT and Smart City Data Sharing

(Kusne)

ML to Integrate Multiple Data Types to Characterize

Billions of Reference Values in NIST Human Genome

Reference Materials (Zook)

NIST Genetic Sensor Foundry (Ross)

Material Design Toolkit (Li)

Materials Informatics/Semi-Automatic Curation (Dima,

Becker)

OAR (Open Access to Research) – NIST Science Data

Repository System (Greene)

Physically Inspired, Highly Transferable Neural Network

Interatomic Potentials (Tavazza)

Polymer Property Predictor and Database (Audus)

AI/ML Bootcamps

AI/ML Bi-Weekly Interest Group

ML and ClassifiersBrian de Cost (PhD work with Holm, Jain, and Rollett)

Construct VLAD representations

1: select

image features

2: characterize

image features

3: encode

image features

B. DeCost, H. Jain, A. Rollett, and E. Holm

JOM (2016). doi:10.1007/s11837-016-2226-1Vector of Locally Aggregated Descriptors

Powder lot classification

60

Platform: ARCAM

B. DeCost, H. Jain, A. Rollett, and E. Holm

JOM (2016). doi:10.1007/s11837-016-2226-1

SEM powder classification

Cross-validation results

B. DeCost, H. Jain, A. Rollett, and E. Holm

JOM (2016). doi:10.1007/s11837-016-2226-1

independent samples

ML and Autonomous Materials Science

A. Gilad Kusne and Collaborators

Phase Mapping: High-Throughput Approach (APL Materials 2016)

Fabricate hundreds-thousands of samples -> HiTp

Synthesis

Measure all samples -> HiTp Characterization

Rapid phase mapping -> Machine Learning

Co

Fe

Ni

Combi Library for Ternary Spread

Diffraction Patterns

XRD

Estimated Phase Map

ClusterAnalysis

Co Ni

Fe

Phase Mapping: High-Throughput Approach

Measurement is a time / resource sink

For wafer of 500+ samples:

In Lab: Takes weeks-months

Synchrotron: Takes 5+ Hours (Every second

counts)

Mn-Ni-Ge library535 samples

Stanford Synchrotron Radiation Lightsource30 seconds per sample4.5 hours

Bruker D830 Minutes per sample2 weeks!

Autonomous Phase Mapping

Autonomous F-scoreSequential F-score

Estimated phase boundary

Theory-based sample

Query

Measured samples

Mis

clas

sifi

cati

on

P

rob

abili

ty

AI is controlling X-ray diffraction systems at SLAC & in the lab!

Solution

Why use AI to just analyze data? Put it on control of the equipment!

Automated Phase Mapping

Synthesize Characterize Analyze

Test case:Combinatorial Library

Physics Knowledge& Databases

Now: Place AI in control of Synthesis.

Questions?