AIと科学者の協創による材料開発...AIと科学者の協創による材料開発...

AIと科学者の協創による材料開発

2018年10月12日

HPCI 第6回材料系ワークショップ

大規模シミュレーションと機械学習の新展開

日本電気株式会社（ＮＥＣ）システムプラットフォーム研究所

国立研究開発法人科学技術振興機構（ＪＳＴ）さきがけ研究員

岩崎悠真

(E-mail : [email protected])

2 © NEC Corporation 2017

自己紹介

名前：岩崎悠真（いわさきゆうま）年齢：32才

経歴：

～2011年東京大学大学院理学系研究科物理学専攻表面・界面の研究（物性実験）（第一原理計算）

2011年～NEC システムプラットフォーム研究所人工知能/機械学習の研究（機械学習）

2015年～2016年University of Maryland(UMD)National Institute of Standard and Technology(NIST)マテリアルズ・インフォマティクスの機械学習アルゴリズムの研究

2017年～科学技術振興機構（JST）さきがけ先進的マテリアルズ・インフォマティクスの基盤技術


Outline

■イントロダクション

ＡＩとマテリアルズ・インフォマティクス

■ＡＩの応用事例1Explainable AI(異種混合学習)による熱電材料開発

■ＡＩの応用事例2木探索型ベイズ最適化による磁性合金探索

■おわりに


マテリアルズ・インフォマティクス

新材料/新現象の発見

Energy Infrastructure Life-Science IoT

主役アシスト

人工知能の技術を材料開発に活用


機械学習（ＡＩ）とは

大雑把にいうと,機械学習(ＡＩ)とは,y=f(x1,x2,x3,,,) のモデルをData-Drivenで作る手法

※そうではない機械学習もある

𝑦 = 𝑓(𝑥1, 𝑥2, 𝑥3, , , )

モデル（関数）説明変数

x1

x2

x3

x4

y

目的変数

・ディープラーニング（深層学習）・ニューラルネットワーク・サポートベクターマシン・ランダムフォレスト etc

…


Outline





■おわりに


Introduction : NECのマテリアルズ・インフォマティクス

新材料/新現象の発見

Energy Infrastructure Life-Science IoT

※1 澤田亮人, 岩崎悠真, 石田真彦: 第78回応用物理学会秋季学術講演会予稿集 5p-C18-4 (2017).※2 R. Fujimaki et al.: In AISTATS, 2012※3 Y. Iwasaki et al.: Nature Partner Journal Computational Materials 3, 4 (2017)

本日の講演内容その1

熱電材料（異常ネルンスト材料）


Anomalous Nernst Effect (ANE)

Spin current js

Electric current je

V

Substrate

Z

X

Y

Magnetic film (e.g. Fe3O4, FePt, etc)

Lower fabrication cost due to its simple structure

Temperaturegradient ∇T

Magneticfield M

-20 -10 0 10 20Magnetic field (kOe)

⊿T=+5 K

⊿T=-5 K

⊿T=0 K

voltage

10 uV


Anomalous Nernst Effect (ANE)

Spin current js

Electric current je

V

Substrate

Z

X

Y

Magnetic film (e.g. Fe3O4, FePt, etc)

Lower fabrication cost due to its simple structure


Magneticfield M

-20 -10 0 10 20Magnetic field (kOe)

⊿T=+5 K

⊿T=-5 K

⊿T=0 K

voltage

10 uV

The ANE is really complicated phenomenabecause of the coupling of spin-charge-heat currents

(Spin caloritronics)

Inductive (data-driven) approachby machine learning


Overview of our materials informatics for ANE material

異種混合学習 (FAB/HMEs)

,,,,, __M

momentspinM

ncompositioPt

ncompositioM

ncompositioM

momentspinPt

ncompositioM

ncompositio DDDDDDDANE fV

Combinatorial Experiment

Composition x

Pt rich

Fe richCo richNi rich

High-Throughput ab-Initio

Calculated with KKR-CPAFe100-xPtx / Si

Co100-xPtx / Si

Ni100-xPtx / Si

20

40

60

80

100

120

140

VAN

E(u

V/K

)

【Descriptors】

and their product term.

Total is 119 descriptors.

Pt

momentspin

M

momentorbital

M

momentspin

Pt

ncompositio

M

ncompositio

D

D

D

D

D

_

_

_

Pt

fermidifferencedownup

M


Pt

onpolarizatispin

M

onpolarizatispin

Pt

momentorbital

D

D

D

D

D

__/

__/

_

_

_

Pt

eractionorbitspin

M

momentspintotal

Pt

fermitotaldownup

M

fermitotaldownup

D

D

D

D

int__

__

__/

__/


Compensated descriptors of Fe1-xPtx

Pt

ncompositio

M

momentspin

M

ncompositio

M

momentspin

Pt

ncompositio

Pt

ncompositio

M

ncompositio

Pt

ncompositio

M

ncompositio

M

ncompositio

momentspintotal

eractionorbitspin

Pt

fermitotaldownup

M

fermitotaldownup

Pt


M


Pt

onpolarizatispin

M

onpolarizatispin

Pt

momentorbital

Pt

momentspin

M

momentorbital

M

momentspin

Pt

ncompositio

M

ncompositio

DD

DD

DD

DD

DD

D

D

D

D

D

D

D

D

D

D

D

D

D

D

_

_

__

int__

__/

__/

__/

__/

_

_

_

_

_

_

M

ncompositio

Pt

momentorbital

Pt

momentspin

Pt

momentspin

M

momentorbital

Pt

momentspin

M

momentspin

Pt

momentspin

Pt

ncompositio

Pt

momentspin

M

ncompositio

Pt

momentspin

M

momentorbital

M

momentorbital

M

momentspin

M

momentorbital

Pt

ncompositio

M

momentorbital

M

ncompositio

M

momentorbital

M

momentspin

M

momentspin

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

_

__

__

__

_

_

__

__

_

_

__

Pt

ncompositio

M

onpolarizatispin

M

ncompositio

M

onpolarizatispin

Pt

momentorbital

Pt

momentorbital

Pt

momentspin

Pt

momentorbital

M

momentorbital

Pt

momentorbital

M

momentspin

Pt

momentorbital

Pt

ncompositio

Pt

momentorbital

DD

DD

DD

DD

DD

DD

DD

_

_

__

__

__

__

_

Pt

momentspin

M


M

momentorbital

M


M

momentspin

M


Pt

ncompositio

M


M

ncompositio

M


Pt

onpolarizatispin

Pt

onpolarizatispin

M

onpolarizatispin

Pt

onpolarizatispin

Pt

momentorbital

Pt

onpolarizatispin

Pt

momentspin

Pt

onpolarizatispin

M

momentorbital

Pt

onpolarizatispin

M

momentspin

Pt

onpolarizatispin

Pt

ncompositio

Pt

onpolarizatispin

M

ncompositio

Pt

onpolarizatispin

M

onpolarizatispin

M

onpolarizatispin

Pt

momentorbital

M

onpolarizatispin

Pt

momentspin

M

onpolarizatispin

M

momentorbital

M

onpolarizatispin

M

momentspin

M

onpolarizatispin

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

___/

___/

___/

__/

__/

__

__

__

__

__

__

_

_

__

__

__

__

__

M

ncompositio

Pt


M


M


Pt

onpolarizatispin

M


M

onpolarizatispin

M


Pt

momentorbital

M


DD

DD

DD

DD

DD

__/

__/__/

___/

___/

___/

M

momentorbital

M

fermitotaldownup

M

momentspin

M

fermitotaldownup

Pt

ncompositio

M

fermitotaldownup

M

ncompositio

M

fermitotaldownup

Pt


Pt


M


Pt


Pt

onpolarizatispin

Pt


M

onpolarizatispin

Pt


Pt

momentorbital

Pt


Pt

momentspin

Pt


M

momentorbital

Pt


M

momentspin

Pt


Pt

ncompositio

Pt


DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

___/

___/

__/

__/

__/__/

__/__/

___/

___/

___/

___/

___/

___/

__/

Pt


Pt

fermitotaldownup

M


Pt

fermitotaldownup

Pt

onpolarizatispin

Pt

fermitotaldownup

M

onpolarizatispin

Pt

fermitotaldownup

Pt

momentorbital

Pt

fermitotaldownup

Pt

momentspin

Pt

fermitotaldownup

M

momentorbital

Pt

fermitotaldownup

M

momentspin

Pt

fermitotaldownup

Pt

ncompositio

Pt

fermitotaldownup

M

ncompositio

Pt

fermitotaldownup

M

fermitotaldownup

M

fermitotaldownup

Pt


M

fermitotaldownup

M


M

fermitotaldownup

Pt

onpolarizatispin

M

fermitotaldownup

M

onpolarizatispin

M

fermitotaldownup

Pt

momentorbital

M

fermitotaldownup

Pt

momentspin

M

fermitotaldownup

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

__/__/

__/__/

___/

___/

___/

___/

___/

___/

__/

__/

__/__/

__\__/

__/__/

___/

___/

___/

___/

M

momentspintotalmomentspintotal

eractionorbitspinmomentspintotal

Pt

fermitotaldowmupmomentspintotal

M


Pt

fermidifferencedowmupmomentspintotal

M


Pt

onpolarizatispinmomentspintotal

M


Pt

momentorbitalmomentspintotal

Pt

momentspinmomentspintotal

M


M


Pt

ncompositiomomentspintotal

M


eractionorbitspineractionorbitspin

Pt

fermitotaldownuperactionorbitspin

M


Pt

fermidifferencedowmuperactionorbitspin

M

fermidifferencedownuperactionorbitspin

Pt

onpolarizatispineractionorbitspin

M


Pt

momentorbitaleractionorbitspin

Pt

momentspineractionorbitspin

M


M


Pt

ncompositioeractionorbitspin

M


Pt

fermitotaldownup

Pt

fermitotaldownup

M

fermitotaldownup

Pt

fermitotaldownup

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

____

int____

__/__

__/__

__/__

__/__

___

___

___

___

___

___

__

__

int__int__

__/int__

__/int__

__/int__

__/int__

_int__

_int__

_int__

_int__

_int__

_int__

int__

int__

__/__/

__/__/

No

rma

lize

d d

escrip

tors

(a

.u.)

composition

Fe1-xPtxFe

rich

Pt

rich


Compensated descriptors of Co1-xPtx

Pt

ncompositio

M

momentspin

M

ncompositio

M

momentspin

Pt

ncompositio

Pt

ncompositio

M

ncompositio

Pt

ncompositio

M

ncompositio

M

ncompositio

momentspintotal

eractionorbitspin

Pt

fermitotaldownup

M

fermitotaldownup

Pt


M


Pt

onpolarizatispin

M

onpolarizatispin

Pt

momentorbital

Pt

momentspin

M

momentorbital

M

momentspin

Pt

ncompositio

M

ncompositio

DD

DD

DD

DD

DD

D

D

D

D

D

D

D

D

D

D

D

D

D

D

_

_

__

int__

__/

__/

__/

__/

_

_

_

_

_

_

M

ncompositio

Pt

momentorbital

Pt

momentspin

Pt

momentspin

M

momentorbital

Pt

momentspin

M

momentspin

Pt

momentspin

Pt

ncompositio

Pt

momentspin

M

ncompositio

Pt

momentspin

M

momentorbital

M

momentorbital

M

momentspin

M

momentorbital

Pt

ncompositio

M

momentorbital

M

ncompositio

M

momentorbital

M

momentspin

M

momentspin

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

_

__

__

__

_

_

__

__

_

_

__

Pt

ncompositio

M

onpolarizatispin

M

ncompositio

M

onpolarizatispin

Pt

momentorbital

Pt

momentorbital

Pt

momentspin

Pt

momentorbital

M

momentorbital

Pt

momentorbital

M

momentspin

Pt

momentorbital

Pt

ncompositio

Pt

momentorbital

DD

DD

DD

DD

DD

DD

DD

_

_

__

__

__

__

_

Pt

momentspin

M


M

momentorbital

M


M

momentspin

M


Pt

ncompositio

M


M

ncompositio

M


Pt

onpolarizatispin

Pt

onpolarizatispin

M

onpolarizatispin

Pt

onpolarizatispin

Pt

momentorbital

Pt

onpolarizatispin

Pt

momentspin

Pt

onpolarizatispin

M

momentorbital

Pt

onpolarizatispin

M

momentspin

Pt

onpolarizatispin

Pt

ncompositio

Pt

onpolarizatispin

M

ncompositio

Pt

onpolarizatispin

M

onpolarizatispin

M

onpolarizatispin

Pt

momentorbital

M

onpolarizatispin

Pt

momentspin

M

onpolarizatispin

M

momentorbital

M

onpolarizatispin

M

momentspin

M

onpolarizatispin

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

___/

___/

___/

__/

__/

__

__

__

__

__

__

_

_

__

__

__

__

__

M

ncompositio

Pt


M


M


Pt

onpolarizatispin

M


M

onpolarizatispin

M


Pt

momentorbital

M


DD

DD

DD

DD

DD

__/

__/__/

___/

___/

___/

M

momentorbital

M

fermitotaldownup

M

momentspin

M

fermitotaldownup

Pt

ncompositio

M

fermitotaldownup

M

ncompositio

M

fermitotaldownup

Pt


Pt


M


Pt


Pt

onpolarizatispin

Pt


M

onpolarizatispin

Pt


Pt

momentorbital

Pt


Pt

momentspin

Pt


M

momentorbital

Pt


M

momentspin

Pt


Pt

ncompositio

Pt


DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

___/

___/

__/

__/

__/__/

__/__/

___/

___/

___/

___/

___/

___/

__/

Pt


Pt

fermitotaldownup

M


Pt

fermitotaldownup

Pt

onpolarizatispin

Pt

fermitotaldownup

M

onpolarizatispin

Pt

fermitotaldownup

Pt

momentorbital

Pt

fermitotaldownup

Pt

momentspin

Pt

fermitotaldownup

M

momentorbital

Pt

fermitotaldownup

M

momentspin

Pt

fermitotaldownup

Pt

ncompositio

Pt

fermitotaldownup

M

ncompositio

Pt

fermitotaldownup

M

fermitotaldownup

M

fermitotaldownup

Pt


M

fermitotaldownup

M


M

fermitotaldownup

Pt

onpolarizatispin

M

fermitotaldownup

M

onpolarizatispin

M

fermitotaldownup

Pt

momentorbital

M

fermitotaldownup

Pt

momentspin

M

fermitotaldownup

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

__/__/

__/__/

___/

___/

___/

___/

___/

___/

__/

__/

__/__/

__\__/

__/__/

___/

___/

___/

___/

M



Pt


M


Pt


M


Pt


M


Pt


Pt


M


M


Pt


M



Pt


M


Pt


M


Pt


M


Pt


Pt


M


M


Pt


M


Pt

fermitotaldownup

Pt

fermitotaldownup

M

fermitotaldownup

Pt

fermitotaldownup

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

____

int____

__/__

__/__

__/__

__/__

___

___

___

___

___

___

__

__

int__int__

__/int__

__/int__

__/int__

__/int__

_int__

_int__

_int__

_int__

_int__

_int__

int__

int__

__/__/

__/__/

No

rma

lize

d d

escrip

tors

(a

.u.)

composition

Co1-xPtxCo

rich

Pt

rich


Compensated descriptors of Ni1-xPtx

Pt

ncompositio

M

momentspin

M

ncompositio

M

momentspin

Pt

ncompositio

Pt

ncompositio

M

ncompositio

Pt

ncompositio

M

ncompositio

M

ncompositio

momentspintotal

eractionorbitspin

Pt

fermitotaldownup

M

fermitotaldownup

Pt


M


Pt

onpolarizatispin

M

onpolarizatispin

Pt

momentorbital

Pt

momentspin

M

momentorbital

M

momentspin

Pt

ncompositio

M

ncompositio

DD

DD

DD

DD

DD

D

D

D

D

D

D

D

D

D

D

D

D

D

D

_

_

__

int__

__/

__/

__/

__/

_

_

_

_

_

_

M

ncompositio

Pt

momentorbital

Pt

momentspin

Pt

momentspin

M

momentorbital

Pt

momentspin

M

momentspin

Pt

momentspin

Pt

ncompositio

Pt

momentspin

M

ncompositio

Pt

momentspin

M

momentorbital

M

momentorbital

M

momentspin

M

momentorbital

Pt

ncompositio

M

momentorbital

M

ncompositio

M

momentorbital

M

momentspin

M

momentspin

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

_

__

__

__

_

_

__

__

_

_

__

Pt

ncompositio

M

onpolarizatispin

M

ncompositio

M

onpolarizatispin

Pt

momentorbital

Pt

momentorbital

Pt

momentspin

Pt

momentorbital

M

momentorbital

Pt

momentorbital

M

momentspin

Pt

momentorbital

Pt

ncompositio

Pt

momentorbital

DD

DD

DD

DD

DD

DD

DD

_

_

__

__

__

__

_

Pt

momentspin

M


M

momentorbital

M


M

momentspin

M


Pt

ncompositio

M


M

ncompositio

M


Pt

onpolarizatispin

Pt

onpolarizatispin

M

onpolarizatispin

Pt

onpolarizatispin

Pt

momentorbital

Pt

onpolarizatispin

Pt

momentspin

Pt

onpolarizatispin

M

momentorbital

Pt

onpolarizatispin

M

momentspin

Pt

onpolarizatispin

Pt

ncompositio

Pt

onpolarizatispin

M

ncompositio

Pt

onpolarizatispin

M

onpolarizatispin

M

onpolarizatispin

Pt

momentorbital

M

onpolarizatispin

Pt

momentspin

M

onpolarizatispin

M

momentorbital

M

onpolarizatispin

M

momentspin

M

onpolarizatispin

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

___/

___/

___/

__/

__/

__

__

__

__

__

__

_

_

__

__

__

__

__

M

ncompositio

Pt


M


M


Pt

onpolarizatispin

M


M

onpolarizatispin

M


Pt

momentorbital

M


DD

DD

DD

DD

DD

__/

__/__/

___/

___/

___/

M

momentorbital

M

fermitotaldownup

M

momentspin

M

fermitotaldownup

Pt

ncompositio

M

fermitotaldownup

M

ncompositio

M

fermitotaldownup

Pt


Pt


M


Pt


Pt

onpolarizatispin

Pt


M

onpolarizatispin

Pt


Pt

momentorbital

Pt


Pt

momentspin

Pt


M

momentorbital

Pt


M

momentspin

Pt


Pt

ncompositio

Pt


DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

___/

___/

__/

__/

__/__/

__/__/

___/

___/

___/

___/

___/

___/

__/

Pt


Pt

fermitotaldownup

M


Pt

fermitotaldownup

Pt

onpolarizatispin

Pt

fermitotaldownup

M

onpolarizatispin

Pt

fermitotaldownup

Pt

momentorbital

Pt

fermitotaldownup

Pt

momentspin

Pt

fermitotaldownup

M

momentorbital

Pt

fermitotaldownup

M

momentspin

Pt

fermitotaldownup

Pt

ncompositio

Pt

fermitotaldownup

M

ncompositio

Pt

fermitotaldownup

M

fermitotaldownup

M

fermitotaldownup

Pt


M

fermitotaldownup

M


M

fermitotaldownup

Pt

onpolarizatispin

M

fermitotaldownup

M

onpolarizatispin

M

fermitotaldownup

Pt

momentorbital

M

fermitotaldownup

Pt

momentspin

M

fermitotaldownup

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

__/__/

__/__/

___/

___/

___/

___/

___/

___/

__/

__/

__/__/

__\__/

__/__/

___/

___/

___/

___/

M



Pt


M


Pt


M


Pt


M


Pt


Pt


M


M


Pt


M



Pt


M


Pt


M


Pt


M


Pt


Pt


M


M


Pt


M


Pt

fermitotaldownup

Pt

fermitotaldownup

M

fermitotaldownup

Pt

fermitotaldownup

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

____

int____

__/__

__/__

__/__

__/__

___

___

___

___

___

___

__

__

int__int__

__/int__

__/int__

__/int__

__/int__

_int__

_int__

_int__

_int__

_int__

_int__

int__

int__

__/__/

__/__/

No

rma

lize

d d

escrip

tors

(a

.u.)

composition

Ni1-xPtxNi

rich

Pt

rich

It is quite difficult for human to disclose the relationship between VANE and bunch of descriptors

(too much data!)

Supervised machine learning

( Deep Learning, SVM, GP, LASSO, DT, RF, etc. )

人間と共創して材料開発できる機械学習は？


Which is the better machine learning for materials science ?

Explainability(White Box)

Predictionaccuracy

Sparsemodelling

Multiple regression

LASSO

Deep Learning(Neural Network)

SVM

Gaussian Process

Random Forest

RVM

Decision Tree

FAB/HMEs


Which is the better machine learning for materials science ?

Explainability(White Box)

Predictionaccuracy

Sparsemodelling

Multiple regression

LASSO

Deep Learning(Neural Network)

SVM

Gaussian Process

Random Forest

RVM

Decision Tree

FAB/HMEs（異種混合学習）

By maximizing a Factorized InformationCriterion (FIC) with a Factorized AsymptoticBayesian inference (FAB), the following threeproblems are solved at the same time.

1. How many data groups ?2. How are the data groups ?3. How are the proper regression equation

for each data groups ?FAB/HMEs

qHND

ND

xypqFIC

gatethj

ej

gatethi

gi

NNN

qq

hme

ji

__

log2

log2

,,logmax,

L0 generalizerfor gating

L0 generalizerfor experts

Entropy

R. Fujimaki et al. Factorized Asymptotic Bayesian Inferencefor Latent Feature Models. In NIPS, 2013.

【Reference】R. Eto et al. Fully-Automatic Bayesian Piecewise SparseLinear Models. In AISTATS, 2014.


Data-driven model for ANE by FAB/HMEs

,,,,, __M

momentspinM

ncompositioPt

ncompositioM

ncompositioM

momentspinPt

ncompositioM


【Visualization of FAB/HMEs result】

1

__/__

1

_int__int__

1

_

2

_

1

2

_

1

_

1

1

_int__

2

_

4

__/__

1

___int__

int__

1

_

1

12

__/

1

__/

1

_

1

1043.51030.6

52.11091.4

1048.31073.1

1066.11045.7

1054.6

1011.11039.5

19.21061.9

52.141.2

1054.51063.7

1079.61034.1

1031.11087.1

M

fermidifferencedownupmomentspimtoal

Pt


M


Pt

ncompositio

Pt

onpolarizatispin

M

ncompositio

Pt

onpolarizatispin

M

onpolarizatispin

Pt

ncompositio

M

momentspinANE

Pt


Pt

ncompositio

Pt

onpolarizatispinANE

M

fermidifferencedownupmomentspintotal

Pt


Pt


M


Pt

ncompositio

Pt

onpolarizatispinANE

M

fermitotaldownup

Pt

ncompositio

Pt

fermitotaldownup

Pt

ncompositio

Pt

onpolarizatispinANE

DD

DDDD

DDDD

DDDV

DDDDV

DD

DDDD

DDDDV

D

DDDDV

Classification Regressioncomponent2

component4

component5

component4

Easy to understand !!


Understanding the data-driven model by material science and physics

,,,,, __M

momentspinM

ncompositioPt

ncompositioM

ncompositioM

momentspinPt

ncompositioM



1

__/__

1

_int__int__

1

_

2

_

1

2

_

1

_

1

1

_int__

2

_

4

__/__

1

___int__

int__

1

_

1

12

__/

1

__/

1

_

1

1043.51030.6

52.11091.4

1048.31073.1

1066.11045.7

1054.6

1011.11039.5

19.21061.9

52.141.2

1054.51063.7

1079.61034.1

1031.11087.1

M


Pt


M


Pt

ncompositio

Pt

onpolarizatispin

M

ncompositio

Pt

onpolarizatispin

M

onpolarizatispin

Pt

ncompositio

M

momentspinANE

Pt


Pt

ncompositio

Pt

onpolarizatispinANE

M


Pt


Pt


M


Pt

ncompositio

Pt

onpolarizatispinANE

M

fermitotaldownup

Pt

ncompositio

Pt

fermitotaldownup

Pt

ncompositio

Pt

onpolarizatispinANE

DD

DDDD

DDDD

DDDV

DDDDV

DD

DDDD

DDDDV

D

DDDDV

Data group Regression equation

component2

component4

component5

component4

Knowledge 1

First of all, the data is classifiedaccording to the total spinmoment

momentspintotalD __

Magnetic

Non magnetic

MagneticMagnetic

It’s trivial because the ANEmaterial should be magnetic.


,,,,, __M

momentspinM

ncompositioPt

ncompositioM

ncompositioM

momentspinPt

ncompositioM



1

__/__

1

_int__int__

1

_

2

_

1

2

_

1

_

1

1

_int__

2

_

4

__/__

1

___int__

int__

1

_

1

12

__/

1

__/

1

_

1

1043.51030.6

52.11091.4

1048.31073.1

1066.11045.7

1054.6

1011.11039.5

19.21061.9

52.141.2

1054.51063.7

1079.61034.1

1031.11087.1

M


Pt


M


Pt

ncompositio

Pt

onpolarizatispin

M

ncompositio

Pt

onpolarizatispin

M

onpolarizatispin

Pt

ncompositio

M

momentspinANE

Pt


Pt

ncompositio

Pt

onpolarizatispinANE

M


Pt


Pt


M


Pt

ncompositio

Pt

onpolarizatispinANE

M

fermitotaldownup

Pt

ncompositio

Pt

fermitotaldownup

Pt

ncompositio

Pt

onpolarizatispinANE

DD

DDDD

DDDD

DDDV

DDDDV

DD

DDDD

DDDDV

D

DDDDV


component2

component4

component5

Knowledge 2

Secondly, the data is classifiedaccording to substrates (Si, SGGGor AlN).

Si

It’s also trivial because the ANEthermoelectric voltage isproportional to the thermalconductivity of substrate

SGGGAlNcomponent4


Magneticfield M

V

substrate

Magnetic film



,,,,, __M

momentspinM

ncompositioPt

ncompositioM

ncompositioM

momentspinPt

ncompositioM



1

__/__

1

_int__int__

1

_

2

_

1

2

_

1

_

1

1

_int__

2

_

4

__/__

1

___int__

int__

1

_

1

12

__/

1

__/

1

_

1

1043.51030.6

52.11091.4

1048.31073.1

1066.11045.7

1054.6

1011.11039.5

19.21061.9

52.141.2

1054.51063.7

1079.61034.1

1031.11087.1

M


Pt


M


Pt

ncompositio

Pt

onpolarizatispin

M

ncompositio

Pt

onpolarizatispin

M

onpolarizatispin

Pt

ncompositio

M

momentspinANE

Pt


Pt

ncompositio

Pt

onpolarizatispinANE

M


Pt


Pt


M


Pt

ncompositio

Pt

onpolarizatispinANE

M

fermitotaldownup

Pt

ncompositio

Pt

fermitotaldownup

Pt

ncompositio

Pt

onpolarizatispinANE

DD

DDDD

DDDD

DDDV

DDDDV

DD

DDDD

DDDDV

D

DDDDV


component2

component4

component5

component4

Easy to understand !!

Non-magnetic

Knowledge 3

All regression equationincludes the product termof Pt spin polarization andPt composition as positive.

Pt

ncompositio

Pt

onpolarizatispin DD _

It’s non-trivial !!


Skew scattering ?Side jump ?Hidden Physics ?

I am discussing with Tohoku Univ. and JAEA

【Suggestion from the AI】

「Increase the Pt spin polarization for better ANE materials」

The AI changed the difficult probleminto the simple problem.


For better ANE material (Ab-initio calculation)D

ensi

ty o

f st

ate

Up spin

Down spin

Co50Pt50

Co

Pt

Co50Pt50N25

Co

Pt

N

Pt

Spin

pola

rization

Composition x(Co50Pt50Nx)

We can synthesize a better ANE materialsby using the knowledge from machine learning.

knowledge : Pt spin polarization should be large.


VANE on CoPtNx /Si (Experiment)

130

150

170

0 5 10 15 20 25 30

110

VA

NE

(uV

/K)

Improvement !

N2 flow during sputtering (sccm)

Co47.9Pt49.3N2.8

Co45.5Pt48.5N6.0

Co45.6Pt47.7N6.7

Co44.9Pt47.0N8.1

Thanks to the explainable AI,we can developed the better ANE material.

特許出願


Outline





■おわりに


Material Development Flow

PropertyCharacterization

MaterialFabrication

HumanThinking

What material next ?

VirtualProperty

Characterization

VirtualMaterialFabrication

AIThinking

…zzz

ab-initio

ab-initio

BayesianOptimization

General Flow by Human

Automated Flowby Computing

Automated flow by integrating KKR-CPA (ab-initio) and game-tree based Bayesian optimization


Bayesian Optimization

σ(x)

μ(x)

Y

X

Acquisition function

𝑎𝑈𝐶𝐵 𝑥= 𝜇 𝑥 + 𝑘𝑁𝜎 𝑥

・Next target material (composition, structure) is selected by searching maximum (minimum) UCB.

・However, this algorithm doesn’t work in multinary compound problem (e.g. CoaFebCrcMndAleSifGeg)

⇒This problem is quite similar to AI-GO (and AI-Shogi) problem.

※UCB : Upper Confidential Bound

e.g. AlphaGO e.g. Ponanza


Game Tree Based Bayesian optimization

・This algorithm explores the better UCB by making the tree structure grow.

・Granularity of the game tree searching is adjusted with the depth of the tree structure.

応用事例～大きなスピン分極率Ｐを持つホイスラー合金の自動探索～

CoaFebCrcMndAleSifGeg

Composition

Spin polarization

𝑃 =𝑛𝐹↑ − 𝑛𝐹

↓

𝑛𝐹↑ + 𝑛𝐹

↓

人工知能(AI)による自動材料スクリーニング～スピン分極率の大きいホイスラー合金の自動探索デモ～

Co Fe Cr Mn Al Si Ge

Next Target CompositionHuman

Spin

Rat

io (nup/n

down)

Epoch

ProgressEF

up spin

down spin

Density of State

Co FeCr MnAl Si

GeTotal

…zzz


Outline





■おわりに


材料関係者とＡＩ関係者の共創

共創岩崎

主役わき役

マテリアルズ・インフォマティクスの主役は、材料のドメイン知識を持った人間です


マテリアルズ・インフォマティクスの主役は人間


AIのプラットフォーム (NEC Advanced Analytics Cloud)


謝辞

【機械学習・AI】

【物理・spincaloritronics】【コンビナトリアル実験】

【MI関連技術全般】

■ University of Maryland (UMD)・Prof. Ichiro Takeuchi・Dr. Sean Wu Fackler・Dr. Tieren Fao etc.

■ 株式会社コメット・李成奇博士 etc.

■ National Institute ofStandard and Technology (NIST)

・Prof. Aaron Gilad Kusne・Dr. Valentin Stanev etc.

■NECデータサイエンス研究所・比嘉亮太 etc.

■ 東京大学・東北大学・齊藤英治教授・吉川貴史助教授 etc.

■原子力研究開発機構・理化学研究所・前川禎通教授 etc.

■物質・材料研究機構 (NIMS)・内田健一 GL etc.

■ NECシステムプラットフォーム研究所・石田真彦・桐原明宏・寺島浩一・澤田亮人・染谷浩子・大森康智 etc.

■ JST・東京大学 (JSTさきがけ/マテインフォ)・常行真司教授 etc.

AIと科学者の協創による材料開発...AIと科学者の協創による材料開発...

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