National Institute for Material Science

実験家からみたマテリアルズインフォマティクスと今後の展望
Impact of Materials Informatics
for Experimentalists and future vision of Materials Research

Toyohiro Chikyow,
Associate General Director,NIMS
Materials Data &Integrated System(MaDiS)
2020.5.28 Tohoku Univ. Seminor
東北大学知のフォーラム セミナー

contents⚫ Impact of Materials Informatics⚫ Present status of “Materials Informatics”⚫ What comes after MI⚫ Materials Research in future⚫ Conclusion

Innovation in materials research：AI can discover
new materials?

2019年2月17日 日経新聞
Nikkei Feb.17, 20192019年2月1日 日刊工業新聞
Nikkan Indust.news Feb.1 2019

A large variety of the composition in materials

Function of materials

Why new discovery is difficult in materials
NewDiscovery

Goal（by tuning）

Starting materials
Number of composing elements

Basic＋AStaring
Basic elements A:impovement
Disadvantage

Basic＋A＋B

Basic＋A＋B＋C

A:Improvement
B:Improvement
disadvantage

A:Improvement
B:Improvement
disadvantage
C:Improvement

A>B>C

How the experts think in materials design.
National Institute for Material Science

Parameter X

Example: Perovskite oxides
Materials data field
New discovery？
Superconductivity

Multiferroic

Experiment result

Parameter YFocusing critical structures and bondsL.Pauling：化学結合論
Nature of chemical Bond
electro negativity=> Heat of formation
W.A Harrison：固体の電子構造と物性Electronic Structure andPerovskite Oxides
the Properties of Solids=> band gap

Band Gap

Electric Structure of Semiconductor : IV elements
CSi

Band Gap Eg

GeSn

How we can extend " Materials Data Field"：
Challenge for High Throughput experimentation
First Screening（Wide range）
各種走査型
プローブ顕微鏡

Second Screening（Narrow range)

New Materials
DiscoveryFirstScreening
Time andCost

1) Calc.
2) Simulation
3) DataScienceNational project on materials informatics“Materials Research by Information Integration” Initiative (MI2I)
See the web site,
http://mits.nims.go.jp/index_en.html“MatNavi” consists of ～20
database (polymer, inorganic
materials, superconductivity,etc.) with high reliability.
“MatNavi” provides datavisualization tools and simple
prediction simulator ofmaterial properties.

New materials discovery by “ Computation”
Points: Materials are reviewed by “ Lattice and elements”

NIMS is involved in“Phase” development
DopingPlasma doping
by Ishigaki, nims

defectAtomicarrangement

Piezoelectricby Ren, nims

LatticedisorderCatalysisby Ye, nims

SuperLatticeSingle, poly amorphous

exisitonphononorbitalSpindielectricstrengthpiezoelectric

luminescence
conductivity

bandgap

magnetization

ThermolectricLight emission Photonics

Design new materials which does not exist before

density
Thermalconductivity

Prof. Gerbrand Ceder(Professor, UC Berkeley)Stefano Curtarolo

Automatic Work Flow from database to calculation◼ Automatic flow to select parameters in DFT Calculation andEnergy integration region◼ Demonstration of quaternary high entropy alloy automatic calculation
for magnetic alloy survey

Visualization of calculated data(Magnetization and CurrieTemperature)
From the data by Dr.Hiori Kino ofNIMS

More than 70,000 data wascalculated for quaternary metal alloys.
•Some metal alloys which contain• Fe,Co Mn showed higher magnetic property
and higher Currie temperature.

How we can use Machine Learning

Input
GaOutputA

C=F(A,B)
As

B

CGaAs
Band Gap
C= d1A1+d2B1+d3A2+d4B2+・・・・・・
A1・・・、B1・・・・:Descriptors

Descriptor library for Machine learning

Automatic despriptor mining
Xeon Py （Python 対応）
Property speculation
Structure stability

Property

New materials discovery by ML③ High Throughput Experimentation
Binary combinatorial synthesis

Ternary combinatorial synthesis

materialCMaterialA

Material
BMovingmask

High throughput Synthesis supported by machine learning-For efficient synthesis with the best condition Experimental Conditions
Machine Learning

Dr.Isao Okudo DataSynthesis

Experimental data
-Automatic synthesis condition determition by BayesianOptimization-Full closed loop system to find the condition-One of the system which is linked to automaticcharacterization tools.

Process Informatics in thin film deposition

Materials Foundry

Theoretical parameters(Yamamura &Tawara eq.)

≒ Descriptors for Machine learing

“ Materials Foundry” as Smart Laboratory System
Materials Foundry
Smart Lab. System

New Materials
Discovery

Idea of Materials

Virtual screeningby Machine Learning
Deep Learning( >10,000)

Real Screening
by automatic
high throughput
experimentaton

How we can handle “ Data”
NIMS Data Platform Center

How the next Genereation Repository
should be ?

Experimental data fromnanotech platform+ metadata
Deposition(data flow )Experimental
data

Measurement
data(TEM,XPS・）+ metadata

Use ( output)

Researchers(NIMS)

Data Plat Form Center
Repository
Published data(SCOPUS etc)

+ metadata

Data Journal

“Smart Laboratory System” from MI to End: One stop Lab

Calculated data set

NIMS Smart Lab
Sequential

Reference Data
AutomaticMI for Materials
designHigh Throughput
HT synthesis

Battery

automatic
Common measurement：XRD, XRF I-V

Systematic flow from MI to Characterization
Machine learning tools

MagnetDielectricCatalysis
DeviceFabricatiomn

Simulation tools

Data format ( DTF5)

・・Points：⚫ From materials design by MI to Characterization＝＞One Stop
⚫ Automation on MI, synthesis and characterization⚫ Common Data format＋data accumulation＝＞Reliable machine learning ＝＞ merit for users

Why we need “ Smart Lab. System”
Devil River

Death Valley:most Risky phase

Research phasemissing phasese
8-year5-year0

Stage 1
Basic R&D

Stage 2development

Materials Informatics

Materials Informatics
Data Driven Materials
DesignJapan：NIMS Mi2i
US: MGI
China: Chinese MGI
EU: NOMAD at al.

Darwin Sea
Industriarizing phase11-yearStage 3
Manufacturing
technology

20-year15-yearStage 4
Production

Stage 5
Efficient
Production

Process Informaics

Challenge: HT+AI+Procss

Robotics in Factory•High Throughput
Experimentation•AI supported Robotics•AI assisted Process

Smart Lab. System

Smartmanufacturing

What comes after “ Materials Informatics”⚫ Small but global⚫ Skillful but innovatie⚫ High-tech
but low cost

INC11: from thePresentation of Peter Simkens
Materials Informatics
Of EU program EXCELIn HORIZON2020⚫ AI⚫ Open Hardware⚫ automation⚫ cloud
manufacturing

世界の潮流１：Materials Innovation Factory at University of Liverpool

Accelerate Materials Discovery by “ Smart System”

summary Data driven materials science will be themajor trend in materials scienceFusion of vertical screening by MI andhigh throughput experimentation will
accelerate new materials discovery
 Materials Informatics as a virtual
screening will be developed to “ Smartsystem” where materials design,synthesis, characterizationand data storage are automaticallygo all out.