2019/8/26

National Institute for Material Science

Research Trend of Data Driven
Materials Research and its impact toMaterials Science

Toyohiro Chikyow, Deputy Director
Center for Material Research by Information Integration（CMi2)
Materials Data Science Group
Materials Data & Integrared System (MaDIS)
National Institute for Materials Science (NIMS)
2019.9.6 CMD Workshop at Osaka Univ.

contents

Materials Research : Past and present
Present status of Materials Informatics
 What is “ machine learning or AI” ?
History ofHigh Throughput Experimentation.
 Materials Research in future Conclusion

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Materials innovation is the keyto realize sustainable society
Wind generator
With supermagnet

Solar panel

Fuel cell in house
Super low powerdevice and long life
batteryMotor with newmagnet
Thermoelectricmaterials

New batterysystem

Lightweighted steel

Value Chain from Materials to Products

Products

Device &system

New Materials

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New Materials Innovation in nano device

Innovation
scaling“Innovation” is driven by “ new 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:impovementDisadvantage

Basic＋A＋B

Basic＋A＋B＋C

A:Improvement
B:Improvement
disadvantage

A:Improvement
B:Improvement
disadvantage
C:Improvement

A>B>C

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Extension of the Data Field& Space
National Institute for Material ScienceY
Unpexcted new materials
Innovation !

goal（by improvement）

start
X

A large variety of the composition4

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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

Latticedisorder

Catalysis
by Ye, nims

SuperLatticeSingle, poly amorphousLong time for getting the results
phononexisitonorbitalSpindielectricstrengthpiezoelectric

luminescence
conductivity

bandgap

magnetization

Thermolectric

density
Thermalconductivity

Light emission Photonics

Design new materials which does not exist before

Data Science as the 4 thExperimentcategory

ExperimentCalculation&Simulation(measurement

Calcualtion& Simulation

Data
Measurement( Information)

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AI, ML CNN and Deep Learning

AIMLCNNDL

But data must be there for the AI

How we can use Machine LearningInput
GaOutputA

C=F(A,B)AsBCBand Gap
C= d1A1+d2B1+d3A2+d4B2+・・・・・・
A1・・・、B1・・・・:Descriptors

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What is the descriptor ?

Descriptor
1) AI people:“Just the parameter to fix the regression. No meaning”

2) Materials Informatics people:“Those are important parameters to determine theproperty of the materials ”

Band Gap

First principle calculation by Prof.Masanobu Nakayama, Nagoya Inst.of Tech7

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Band Gap

Electric Structure of Semiconductor :SiSiConduction BandP orbital

Sp3 hybridizedBand Gap Eg

S orbital
Valence Band82019/8/26

Electric Structure of Semiconductor : IV elements
CSiGe

SnBand Gap Eg

Density of States: C, Si, Ge92019/8/26

Electric Structure : GaAs

Ga

AsConduction bandPP
Sp3 hybridizedBand Gap
Eg

Sp3 hybridizedss
Valence band

Electric Structure of Semiconductor : IV、II-V, II-VI
GeGaAsZnSeConduction Band

Band GapEg

Valence Band10

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How we can know band Gap by ML

Input
GaA

OutputMachine finds the “ Function”

C=F(A,B)As

B

CBand Gap
C= d1A+d2B+d3A+d4B+・・・・・・Descriptors: atomic distance, p energy level s energy level

Schottky Barrier Height and Heat of formation11

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We want to design new materials ! ( Eletronegativity)
Heat of formation ΔHf ∝ X（α）－Y（β）
X(α）： Electronegativity of X :α
Y（β）：Electronegativity of Y : β
From “ Nature of Chemical Bond” by Linus Poling

We want to design new materials !

ΔHf ∝ electron density

122019/8/26-Materials Genome Initiative-

Materials Informatics(Data‐Driven Materials Research)①Creating of DB②Collection andIntegration of data

Individual
Individual
Experimental ComputationalData
Data

Bibliographic
DB

MatNavi

IntegratedDB
annotation

・ Verification by ・ Understandingby theory
experiment
feedback toDBs③Data‐mining,
Machine Learning,
Modeling Rulebasedsystem
 Discripter

・ Understanding
・ High‐rules between
throughput
production
・ Selection of candidate
new materials’ structures and system
・ Visualization of correlations
functionsdiscovery④Visualization, Validation
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From the JST-CRDS

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Harvard University :USA

■Clean Energy Project: Prof. Alán Aspuru-Guzik
The Clean Energy Project (CEP) is a virtual high-throughput
discovery and design effort for the next generation of plastic solar
cell materials.

 Organic device materials
 Calculation data

The NOMAD Laboratory
A European Centre of Excellence

CSC is part of two recently funded European Union's Horizon 2020 projects.
The NoMad (Novel Materials Discovery) project will develop "big-data analytics"
for materials science. CSC's role in the project is to support the researchers inHPC code optimization and utilization of PRACE systems.

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National project on materials informatics“Materials Research by Information Integration” Initiative (MI2I)

Two approaches to material and device development
Social and urban infrastructures
are based on materials science

Conventional approach
Based on the fundamental
physics, e.g. DFT, weinvestigate material properties .

Solid laser

Direct problem
scheme

GMR headSuperconductivity

Inverse problem
schemeBy getting help of the data
science, we explore materials
having a desirable properties.
Materials Informatics

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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.

National project on materials informatics“Materials Research by Information Integration” Initiative (MI2I)Start : July 1, 2015
Period: 5 years
Budget: about 2 billion JPY

Project leader: Satoshi ITOH (NIMS)

MI2I
Target subjects

Materials informatics
application tools

MagneticBatt
eryNewsubjects

ThermalAB
C

Materials database

MatNavi
Open data‐platformZXY

API/UISociety/IndustryKey point

We not just preparematerials databaseand materials
informatics tools as a
useful elements, butalso we propose asolution scenario for
problem by"connecting the dots".

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Phase Change in Materials Informatics
National Institute for Material Science① Desire forMaterials② Virtual Screening by MI③ HT Screening②´ data set by Calc or DB

Super Computer

DB and data

④ Discovery ofnew materials

Materials Data Science GroupHigh Throughexperimentation︓Dr.Chikyow

Materials design
by machine
Learning :Dr.Koayma
Dr.KonoHigh Throughput
characterization
by machine learning: Dr.Yamasaki

Structure and property
prediction by informatics:
Dr.KinoAIST（Dr.Takashi Miyake︓magnetic materials）

Osaka Univ.（Prof.Tamio Oguchi︓Structureand property）

U of Tokyo（Prof. Tsuda ︓Data Science）

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Materials Informatics
FunctionSiC• Electric resistivity• Dielectric constant• Hardness
Materials science/
Materials informatics

Materials
database=Open data

Private data,know how(Not opened )Materialspecification• Composite ratio• Crystal structure• Impurity content

Manufacturing process

All atomic species and positions
Unknown data～1024 atoms in a realspecimen
All atomic positions
cannot be determinedin a real material !

③ High Throughput Experimentation
Binary combinatorial synthesis

materialC

Ternary combinatorial synthesis

MaterialA

Material
BMovingmask19

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Combinatorial I-V and C-V measurement
Probe
combi film

Ternary composition spread film
Al ElectrodeＡSi(two terminal )Al Electrode

Ｂ

Ｃ
Measurement points : 500

Equipements
・ Semiconductor parameter (I-V)
・ LCR meter(C-V, f = 1MHz)

ハイスループット計測例︓HfO2-Y2O3-Al2O3 I-V, C-V測定

Leakage Current

Capacitance

Flatband Voltage Shift

Hysteresis Width(film thickness:10nm)

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National project on materials informatics“Materials Research by Information Integration” Initiative (MI2I)
Construction of “all‐Japan” structure , “connecting the dots”“all‐Japan” by inter‐regional cooperation• NIMS/JST as hub organization• Academia network・・・Hokkaido Univ.,
Tohoku Univ., Univ.Tokyo, TUT, JAIST,
Nagoya Inst.Tech., Kyoto Univ., Osaka
Univ., Hiroshima Univ., AIST, IMS, JFCC
• Translational research satellites

北大Network of organizations
北陸先端大

東北大

広大 京大
“all‐Japan” by inter‐sector cooperation• Industry‐government‐academia cooperation• A new scheme of multi‐client collaboration

産総研
阪大 統数研 東大
名大
東工大
名工大
ＪＦＣＣ

Government/ participation of Municipalities
MI2I consortium ( 4/1/2016 ～ ）• Collaboration in equal partnership of participants• Sharing of information and achievements as possible

NREL : Open experimental data21

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最近のトレンド：Materials Innovation Factory at University of Liverpool

How can collect data：Citrine22

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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 Descriptors give us hints to think different
direction＝＞from “ Black box “ to “ White box”

MEGA

EVOLUTIONof

10/28
11/ 1

MATERIALS

10/28

NIMS Open HouseMon@Tsukuba

30

Wed

1

＼ 80 Advanced Labs ／Open House: Lab & Facility<Enterprise / University students>
Guided Lab Tours / Lectures on Materials Scienceetc.

Materials Innovations Driven by AI + DATA!
Winning
L ectur e

Pioneer of data driven materials science Prof. Gerbrand Ceder （UC Berkeley）
Global material database editor
Dr. Pierre Villars（MPDS)NIMS Technology TransferThu@Tokyo11/FriNIMS Award / Academic Symposium@Tokyo31~NIMS WEEK 2019Mon

＼ 80 Research topics ／Latest Research Exhibition 2019
InvitedLecture

Launched Research center with NIMS
“HondaJet” Engine development

Mr. Sanford Browne (L'Oréal)
Mr. Yoshihiko Wajima (Honda R&D )STAM 20th Anniversary Symposium

Fri@Tokyo

Open Science and Materials Research
Evolve into Data journal / Material development using Open Data

Admission Free WEB Registration Required

NIMS WEEKNational Institute for Materials Science (NIMS)

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