【Nano tech 2024 Special Symposium】
How MI has changed Nanomaterials Development -Current Issues and Future Prospects-

Applications and Challenges in
Computational Materials Developmentusing Atomistic Simulator Matlantis
Preferred Computational Chemistry
Senior Manager Akihiro Nagoya
・2024 2/1 Thu. 15:00-15:40
・Main Theater［East hall 4］1

self-introduction
Akihiro Nagoya
Preferred Computational Chemistry (PFCC)
Senior Manager, Technical Sales & Customer Success Department
Profile●Completed Master's program, Graduate School of Engineering Science, OsakaUniversity (2007)●Toyota Central R&D Labs.
○First-principles calculations: solar cell materials, semiconductor materials, fuelcell catalysts, graphene materials○●ENEOS Corporation (2022)○●Classical MD and MI of polymers

Matlantis related services

Preferred Computational Chemistry (2023)

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Agenda● About us● Matlantis™: High-speed universal atomistic simulator●

User Case Study○ Matlantis for Elucidation of Complex Phenomena and Screening of Materials(Dr. Onodera, ENEOS)●●

Challenges and Perspectives
1.

useability

2.

overseas3.

Difference in scale from experimentSummary
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PreFerred Computational Chemistry（PFCC）

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About Us
基本情報Company Name
Preferred Computational Chemistry

RepresentativeDr. Daisuke Okanohara(CEO)Established
June 1st, 2021
*July 2023 rated 9.4/10 by IDTechEX● Mission“To accelerate materials discovery for a sustainablefuture. ”● Product：Matlantis™: High-speed universal
Japan’s AI technology leader.

The largest petroleum company. atomistic simulator
5Matlantis™: High-speed universal atomistic simulator

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Challenges in Materials Science1“To accelerate materials discovery for a sustainable future. ”
1060 of unknown molecules in the chemical space.

2●●●3Experiment used to be the mainstream but has faced throughput limitation.
Computational Simulation (e.g., DFT) is often too computationally costly tobe used in practical situations.
Materials Informatics is a promising data-driven approach; however,“Universality” has always been challenging.

Simulation × Deep Learning

Materials Informatics
Mr. Shibata gave a talk at NanoTech 2023 (Speaker Deck)

https://speakerdeck.com/matlantis/ri-ben-yasasii-materiaruzuinhuomateikusuhenodao-ki-chai-tian-nan
otech2023

Background
Materials Informatics (MI)●The technology to accelerate the discovery and development of new materials using AI.

●Avoid the conventional trial-and-error approach without relying on the intuition or experience of researchers.

Conventional

MIMatlantis

Experiment~10 times/monthExperiments and calculations→ Virtual experiments

Calculations → Virtual experiments →Experiment
In: Atomic positions
Out: Energy and Forces

database

deep learningmodel

simulator~10 times/monthThousands of times/month
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Universal simulator for predicting material properties
from atomic configurations

製品の特長2,000 years

72 elements

20M times

PFN supercomputing cluster

2,000 GPU years have been spentcollecting DFT data sets.

Applicable to 72 elements and more.

Up to 20M times faster thantypical DFT simulation.

Pre-trained AI model: no training data preparation or AI
knowledge required
Maintenance free: No system/hardware specialist required
10Why Matlantis™?
DFT Calculation
Modeling

Interpretation

Simulation

Experiment

Experiment

Experiment

Experiment

Experiment

Experiment・Too long calculation time.
・Simplified simulation model and condition.
・Experiment followed by/parallel to simulation.
~10,000 times
ModelingSim.

Interpretation

Experiment・Blazingly faster than DFT.
・Large-scale model, long physical time.
・Simulation-driven approach; iterative trial & error prior to experiment.
・Dramatically accelerate the material development process.

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Applications
Catalyst

Large scale screening for
optimal dopant species

Battery

Li diffusion in solid electrolyte

Ceramics

LubricantAdsorbent

Adsorption energy in MOF

Tribochemical reaction

Semiconductor
Magnetic Materials
Separation Membrane
Metal＆Alloy
Reaction analysis oftrimethylaluminum…

Phase transition in BaTiO3

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Our Services
Core Technologies

Usability

Jupyter Lab
Environment
Universal Neural Network Potential

Documents andSample Programs

Customer Support
Cloud Computing Resources
13Matlantis™ ユーザーの声：Toyota Motor Corporation

10 million structures
What took three months inper year.

one week
Highly flexible and easy to apply.

Half of users
are from experiment teamhttps://www.youtube.com/watch?v=vMwllr9v1x4

Research Transformation by Matlantis™

https://www.slideshare.net/Matlantis/matl
antis30-rula05ce05o175xpfcc20221215

Comment from Prof. M. Koyama (Shinsyu Univ) during the press releases.

Previously
Researcher’s time

“ I've been looking at various methods for almost 20 years, but
this is by far the most versatile I've touched. There is nothing
but excitement.”
Computer’s time

Researcher’s time●●

Learn their intuition from experimental scientists
Read papers carefully first●●●

Maintain computer in free time●https://pc.watch.impress.co.jp/docs/news/1336
421.html

Computer’s time

Run the calculations and show them to experimental scientists
To read the paper carefully…
○
Create calculation models for a day/week + automate
job submission○
Automatically analyze the calculation results for aday/week.
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No need to maintain the computer

Scientific Papers

https://matlantis.com/cases

Other publications (as of July 2023)：

Editor’s Highlights on 2
categories

Lieven Bekaert, et al.

2023 ChemSusChem2023, e202300676

Ayako TAMURA, et al.

2023 J. Comput. Chem. Jpn., 21, 129-133

Kan Hatakeyama, et al.

2023 10.26434/chemrxiv-2023-f9lxl

Tien Quang Nguyen, et al.

2023 J. Comput. Chem. Jpn., 21, 111–117

Lieven Bekaert,et al.

2023 J. Phys. Chem. C, 18, 8503–8514

Hiroshi Sampei,et al.

2023 JACS Au, 3, 991–996

Yuji Shitara,et al.

2023 Tribologist, 68, 280-291

Yuji Shitara and Shigeyuki Mori

2022 Tribologist, 67, 662-671

Tasuku Onodera

2022 Tribologist, 67, 821-829

Kaoru Hisama,et al.

2022 Comp. Mat. Sci., 218, 111955

So Takamoto, et al.

2022 Nature Comm.,13, 2991

+37 conference presentations
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Customer Calculation Examples

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Case Study: Material Screening with Matlantis
The 3rd Matlantis User Conference
Excerpts from materials provided by Dr. Onodera at ENEOS corporation

Lubricant additives for improved processability
Experimental results: processability varies with the type of base oil⇒ Diffusion and adsorption dynamics of additives in two different base oils
metalbase oiladditivesbase oilmetalbase oiladditivesn-Paraffin x 80

Dodecanol x 1

base oil
Isoparaffin x 80

additives

metal

Dodecanol x 1

additives

lubricant oil withlower friction→ Fast adsorptionof additives

lubricant oil withhigher friction→ Slow adsorptionof additives

metalpressurepressure

Onodera, Tribologist, 67 (2022) 821 / Yamagishi et al, Tribology Conference 2022 Spring, Tokyo, D25 / Shibata et al, Tribology Conference 2022 Fall, Fukui, A23
Copyright © ENEOS Corporation All Rights Reserved.

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How to find better lubricant oils?
Insights from MD simulations with Matlantis :To reduce frictions, use a base oil
which forms a sparse oil film to facilitate additive adsorption"design guideline"!

Machine learning based screening using simulation data
representing the sparsity of base oil molecules
Copyright © ENEOS Corporation All Rights Reserved.

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Proposal of base oil with low friction

RDF
featurebase oil for processing
Reference oilABC

middlepoint

3.74

3.70

3.68

3.50

inverseslope0.99

0.94

0.96

0.90

Promising candidate base oils for improved
processability(sparse oil film, fast additive adsorption)

densecommercially available compounds

Features (RDF inverse slope)sparseThree candidate compounds that are commercially available.
Experimental verification phase!
Promisingcompounds＞10,000available

isoparaffin
Reference oil★
21● Training data
● Predicted Data
Available compounds
dense

Features (RDF midpoint)sparseCopyright © ENEOS Corporation All Rights Reserved.

Challenges and Prospects

22Issue 1: Usability
Functional Integration between“Advance/NanoLabo“ and "Matlantis"
I want a car.

Cloud services via a browser are difficult to automate.

Python programming is difficult. I want an intuitive GUI.

Training programs with SkillUpNext

I need immediate support by phone.

I can't do it myself, so I want to outsource the simulation.
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Learning Materials and Customer Support
Atomistic Simulation Tutorial●Document:
https://docs.matlantis.com/atomistic-simulation-tutorial/en/●Expert customer support- Inquiry form
- support meetings- Sample programs

Code:
https://github.com/matlantis-pfcc/atomistic-simulation-tutorial

Table of contents●

Chapter1: Introduction●

Chapter2: Structural optimization●

Chapter3: Energy●

Chapter4: Vibration, phonon●

Chapter5: Reaction path analysis●

Chapter6: Molecular dynamics●

Chapter7: Conclusion

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ChatGPT

Issue 2: Overseas Expansion

User base of Matlantis●

Confidential

Available Countries and Regions. (As of January 2024)■ Users exist■ Ready for providing the service27

Issue 3: Difficulties in ScaleDFTMD~ nm
ps - ns

S. G. Louie, Nature Materials, 20, 728 (2021)

Difficulties in Scale: Device Structure of Li-ion Batteries
Materials, Cells, Modules

Y. Chen, Journal of Energy Chemistry 59, 2021, 83-99

Experiments

K. Liu, Automotive Innovation, 5, 2022,121

summary●

Matlantis™ can dramatically accelerate material discovery and mechanism
elucidation of your interest.

●

Matlantis-driven approach will enable you to perform large scale screening formaterials discovery in a realistic time scale.

●

Matlantis™ will facilitate the DX of your R&D department.

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