PFCC special lecture on materials informatics_nanotech2023
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At nano tech 2023, PFCC’s Rabi Shibata gave a special lecture on materials informatics. [Lecture summary] The growing interest in materials informatics (MI) has recently pushed Japanese companies into launching various MI projects, some of which have made successful achievements. At the same time, however, the resulting influx of MI-related information has caused confusion among those who are willing to get into MI. In this lecture, PFCC’s Rabi Shibata gave an overview of the current MI landscape and where PFCC’s universal atomistic simulator Matlantis plays it’s role in the industry. He also introduced his own case study to illustrate what motivates materials scientists to take up MI. https://matlantis.com/
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PFCC special lecture on materials informatics_nanotech2023
- 1. The easiest guide to Materials Informatics 1 PFCC Rabi Shibata ※ only the parts that can be made public
- 3. What I want to tell you in advance Points of concern in this lecture 3 1. No deep technical contents 2. Focus on clarity 3. I would be happy to hear your reactions !
- 4. What I aim to achieve through this lecture Understanding the outline of Materials Informatics 4 Before After Researchers who want to start Materials Informatics Understanding the outline of Materials Informatics
- 5. What I felt during my time as a R&D member I want to realize the world where technology can contribute a little more to business. 5 Impact on decision making in corporate activities Customer, Competitor, Company Technology Current Ideal
- 6. Materials Informatics：News The amazing case of MIT-Samsung（2015） 6 https://www.itmedia.co.jp/smartjapan/articles/1508/21/news038.html ・Development of all solid-state lithium-ion battery ・Developed in about 1 year by Materials Informatics ・Japanese company took 5 years to develop hope wish anxiety R&D can be changed by Materials Informatics
- 7. Outline of Materials Informatics 7
- 8. Outline of Materials Informatics To promote materials development by utilizing information processing technology 8 y x1 x2 x3 ・・・ 1 2 3 ・ ・ ・ Original data (train data) already known y x1 x2 x3 ・・・ 1 2 3 ・ ・ ・ ? predict unknown y Data to be predicted (test data) evaluate y = f(X) already known
- 9. Materials Informatics：Issue ① ① Do you have enough original data？ 9 y x1 x2 x3 ・・・ 1 2 3 ・ ・ ・ already known y x1 x2 x3 ・・・ 1 2 3 ・ ・ ・ ? predict unknown y evaluate y = f(X) already known ・ less amount of data ・ Not unified way of collecting data ・ Difficulty in data shaping Original data (train data) Data to be predicted (test data)
- 10. Materials Informatics：Issue ② ② Is the model accurate enough? 10 y x1 x2 x3 ・・・ 1 2 3 ・ ・ ・ already known y x1 x2 x3 ・・・ 1 2 3 ・ ・ ・ ? predict unknown y evaluate y = f(X) already known ・ less amount of data ・ Not unified way of collecting data ・ Difficulty in data shaping ・ Lack of Data Science skill ・Lack of knowledge on Material development ・Ambiguous problem setting Original data (train data) Data to be predicted (test data)
- 11. Materials Informatics：Issue ③ ③ Is the model's coverage sufficient? 11 y x1 x2 x3 ・・・ 1 2 3 ・ ・ ・ already known y x1 x2 x3 ・・・ 1 2 3 ・ ・ ・ ? predict unknown y evaluate y = f(X) already known ・ less amount of data ・ Not unified way of collecting data ・ Difficulty in data shaping ・ Limited range of reliable prediction ・ Failure to meet R&D expectations ・ Searching for databases Original data (train data) Data to be predicted (test data)
- 12. What causes these issues in Material Informatics 12 y x1 x2 x3 ・・・ 1 2 3 ・ ・ ・ y x1 x2 x3 ・・・ 1 2 3 ・ ・ ・ ? y = f(X) ・Experiment data ・Literature data ・Database etc. already known already known predict unknown y This approach is based on the condition that we have enough amount & quality of original data. Original data (train data) Data to be predicted (test data)
- 13. Continuation of activities with perseverance in the face of challenges. As a result, the number of related cases/news is increasing. 13 Showa Denko reduced number of experiments in Materials Development by 25% with AI Prediction（2020） https://www.nikkei.com/article/DGXMZO58115270W0A410C2000000/ Reduce the number of experiment Materials Informatics：Current Efforts ①
- 14. 14 600 of MI expert person will be trained in Asahikase（2021） https://xtech.nikkei.com/atcl/nxt/column/18/00001/06113/ In-house education of MI Materials Informatics：Current Efforts ② Continuation of activities with perseverance in the face of challenges. As a result, the number of related cases/news is increasing.
- 15. 15 Sumitomo Rubber accelerates material development with Toyota's MI Service（2022） https://monoist.itmedia.co.jp/mn/articles/2204/14/news045_2.html Success with comercial MI service Materials Informatics：Current Efforts ③ Continuation of activities with perseverance in the face of challenges. As a result, the number of related cases/news is increasing.
- 16. 16 Collecting data Shaping data Modeling Prediction Evaluation Issue ①enough data? ②accuracy？ ③reliable range？ In-house External services education system education tool analysis advisery 8~10 20 < 5~8 3 3~5 8~10 5~6 1 4~5 4~6 Investigation by PFCC Shibata (Dec, 2022) Continuation of activities with perseverance in the face of challenges. As a result, the number of related cases/news is increasing. Outline of Materials Informatics In Japan
- 17. What causes these issues in Material Informatics 17 y x1 x2 x3 ・・・ 1 2 3 ・ ・ ・ y x1 x2 x3 ・・・ 1 2 3 ・ ・ ・ ? y = f(X) ・Experiment data ・Literature data ・Database etc. already known already known predict unknown y The approach is to have the original data and make predictions based on that data Original data (train data) Data to be predicted (test data)
- 18. Materials Informatics：Another Point of View Use of Simulation 18 Human Computer※ Inductive approach experimental science theoretical science machine learning （～P.17） Simulation No need to collect the original data ※ Actually, human have to collaborate with computer. In addition, machine learning and simulation is often combined. Deductive approach
- 20. Company Name Established June 1, 2021 Address 3rd fl. Otemachi Bldg., 1-6-1 Otemachi, Chiyoda-ku, Tokyo, Japan Representative Daisuke Okanohara (CEO) Mission “To accelerate materials discovery for a sustainable future. ” Product Matlantis™: High-speed universal atomistic simulator 20 About Us The largest petroleum company. https://www.eneos.co.jp/english/ Japan’s AI technology leader. https://www.preferred.jp/en/ Confidential
- 21. Our strong point 21 Core technology of our service has been adopted by nature communications. URL：https://www.nature.com/articles/s41467-022-30687-9
- 22. Matlantis 22
- 23. Basic features of Matlantis ① 23 Matlantis is a simulator that rapidly calculates energy and force with atomic structure. y = f(X) physical properties and phenomena Search for Material by simulation Search for Material by simulation
- 24. Basic features of Matlantis ② 24 Various physical properties and phenomena can be simulated.
- 25. 25 Provided as a cloud service（JupyterLab） Basic features of Matlantis ③
- 26. Strong points of Matlantis ① 26 Condition of first-principles calculations ・solver = QUANTUM ESPRESSO (PWscf) ・ver：6.4.1 ・PP：Pt.pbe-n-kjpaw_psl.1.0.0.UPF ・Ecutoff：40 Ry (≒544 eV) ・Xeon Gold 6254 3.1GHz x 2 (36 cores) ・RAM：384 GB Blazingly faster than conventional DFT calculations
- 27. 27 Applicable to 72 elements and more Strong points of Matlantis ②
- 28. How to realize this simulation (Matlantis) 28 … Energy calculated by Matlantis Energy calculated by DFT More than 20M of DFT simulations for various molecular/crystal structures Unique & versatile Graph Neural Network model (PFP) built by Preferred Networks Iterative model training for accurate data prediction Training data GNN Machine Learning The greatest model is established by enormous amount of data and our know-how.
- 29. Achievements in Material Themes 29 We have case-studies in material themes related to our mission. Catalyst Battery Smiconductor Alloy Lubricant Ceramics Adsorbent Process
- 30. Finally 30
- 31. Relationship between Materials Informatics & Matlantis 31 Summary of this lecture so far Planing Basic Research Product Development Scale up Production Deductive approach Inductive approach Often spoken of in the context of Materials Informatics Human Computer Inductive approach experimental science theoretical science machine learning Simulation Deductive approach
- 32. If I came back to the research for materials science I would want to develop material products with experiment. 9:00 Start to work Team MTG 10:00 Experiment 12:00 Lunch 13:00 MTG 14:00 Experiment Analysis 17:00 18:00 Routine tasks Leave office 32
- 33. 9:00 10:00 12:00 13:00 14:00 17:00 18:00 Update the way of research by DX for R&D and work-style reform Target MTG Routine tasks Experiment Data analysis Approach ・Auto-experiment ・DoE ・Electronic notebook ・Machine learning ・Cut waste ・Mind-change DX for R&D work-style reform 33 If I came back to the research for materials science Start to work Team MTG Experiment Lunch MTG Experiment Analysis Routine tasks Leave office
- 34. Continue to bring up the spirit of enjoyment with simulation 9:00 10:00 12:00 13:00 14:00 17:00 Set Simulation 18:00 Original motivation for computational chemistry ：We want to try out ideas safely spirit of enjoyment 34 Target MTG Routine tasks Experiment Data analysis Approach ・Auto-experiment ・DoE ・Electronic notebook ・Machine learning ・Cut waste ・Mind-change DX for R&D work-style reform Start to work Team MTG Experiment Lunch MTG Experiment Analysis Routine tasks Leave office Planing Basic Research Product Development Scale up Production If I came back to the research for materials science
- 35. 35