ACHEMA2024_High speed neural network for chemicals and materials discovery

This is the presentation material delivered by Rudy Coquet, Senior Manager of Preferred Computational Chemistry, at ACHEMA 2024 which is the world’s leading trade show for the process industries in Germany.

The contents include the following:
- Introduction of Matlantis, our atomic-scale simulator
- Use cases with real-world applications such as catalysts, battery materials and lubricants

Transcript

1. Dr Rudy Coquet 1 High speed neural network for chemicals and materials discovery Topic: #digital: AI and data analytics Session: AI-based modelling and engineering June 11, 2024
2. © Preferred Computational Chemistry, Inc. All Rights Reserved. Agenda 2 ❏ Introduction to our company and product (10’) ❏ Use cases with real-world applications (15’) ❏ Catalysts: Renewable synthetic fuels ❏ Battery materials: Lithium diffusion in solid electrolytes ❏ Lubricants: Base oils in silico screening (Dr Tasuku Onodera, ENEOS Corp.) ❏ Q&A (5’)
3. © Preferred Computational Chemistry, Inc. All Rights Reserved. > Established June 2021 in Tokyo, “to accelerate materials discovery for a sustainable future”, providing Matlantis™, a high-speed universal atomistic simulator. 3 About Us Largest Japanese oil company. https://www.eneos.co.jp/english/ Japan’s AI technology leader. https://www.preferred.jp/en/　 Additionally, since June 2024 :
4. © Preferred Computational Chemistry, Inc. All Rights Reserved. 4 Parent companies Largest Japanese oil company Japan’s AI technology leader - Part of ENEOS Holdings Group - Capital: 30 billion JPY (~191M €) - Operating income: 82 billion JPY in FY2020 (523M €) - 9,348 employees - Founded in March 2014 - Today the largest unlisted AI tech venture in Japan - R&D business with a wide variety of companies - Developed its own processors, called “MN-Core” - Supercomputer ranked #1 in the Green500 list several times PFN supercomputing cluster https://www.preferred.jp/en/　 https://www.eneos.co.jp/english/
5. © Preferred Computational Chemistry, Inc. All Rights Reserved. • Used by 80 organizations (https://matlantis.com/) • 500 licensed users • Industries: Academia, Chemicals, Electronics, Mining, Rubber, Ceramics, Automobiles, Non-ferrous metals, Petroleum, etc. • First academic & commercial organizations outside Japan 5 Business status May 2022 Nov. 2022 2024 2023 July 2021 EU servers From where Matlantis is available US servers Where Matlantis calculations run JP servers …
6. © Preferred Computational Chemistry, Inc. All Rights Reserved. Matlantis ™: a paradigm shift Lin and Wang, Commun Mater 4, 66 (2023) Going from a few hundred atoms in DFT to 10,000 atoms in Matlantis™, one can study realistic systems, opening the door for exploration into a myriad of materials, chemical, and even biological systems. 6
7. © Preferred Computational Chemistry, Inc. All Rights Reserved. Three key features 96 elements 7
8. © Preferred Computational Chemistry, Inc. All Rights Reserved. Accurate - Computes energies and forces from atomic structures with DFT accuracy (cf. use cases) Scalable and secure - No need to install hardware or software. Global standard cloud system (AWS) Flexible and customizable - Programmable environment in JupyterLab (Python) Physical properties calculation library User interface Capabilities / 8
9. © Preferred Computational Chemistry, Inc. All Rights Reserved. Jupyter Notebook Department A Inference System Group Drive Department B Batch Inference GPUs (Tenant ABC, Inc.) *System configurations and specifications are subject to change ABC, Inc. [ Independent for each user ] - # of instances provided = # of active users - No access rights to other users' data - File system independent for each user - 2 CPU, Memory: 8 GB, Disk: 100 GB / user [ Tokens are allocated to each tenant ] - Used for inference of energy, force and charge. - The amount of Tokens varies depending on the contracted plan - If a tenant's usage exceeds the Token limit, the calculation speed will be limited - Optimized total throughput by batch inference system [ Independent for each tenant ] - Accessible by all users in your tenant - Not visible to other tenants - Size may vary by the purchased license System environment 9 Token: function of the number of atoms that can be processed. Given every second.
10. © Preferred Computational Chemistry, Inc. All Rights Reserved. Mechanism behind Matlantis ™ PFN cluster with own MN-Core™ series of deep-learning processors 10
11. © Preferred Computational Chemistry, Inc. All Rights Reserved. Proprietary & Unique Neural Network Potential: TeaNet/PFP Research papers on our NNP: https://www.nature.com/articles/s41467-022-30687-9 https://doi.org/10.1016/j.jmat.2022.12.007 > Our training dataset is generated by molecular dynamics simulation using the machine learning model itself, rather than humans, thereby minimizing the influence of assumed knowledge. > To achieve universality, our dataset includes disordered and metastable structures: this is a unique feature which allows Matlantis™ to predict transition states and correctly represent unknown systems. 11
12. © Preferred Computational Chemistry, Inc. All Rights Reserved. Customer testimonial: Toyota Motor Corporation Geometry optimization of a Li-based battery material by Toyota Motor Corporation 29 times slower than Matlantis 69 times slower than Matlantis Matlantis™ DFT on a national supercomputer DFT on Toyota’s supercomputer x Highest speed “Previously, we had to search our database to find new materials. It took about three months. Matlantis does that in a week. With Matlantis, we can calculate roughly 10 million structures in a year. This can be quite an advantage to find promising candidates.” 12
13. 13 Use cases
14. © Preferred Computational Chemistry, Inc. All Rights Reserved. Catalyst case: Renewable synthetic fuel catalysts https://matlantis.com/en/cases/calculation001/ Aim: Identify dopant species for Fischer-Tropsch catalysts (improve CO dissociation rate by optimizing catalyst promoters) Approach: Screen 9300 configurations with varying compositions (cobalt host metal) Result: Found effective dopant species that lower the activation energy by nearly 40% Calculation time: Two weeks (vs. years by DFT) MAE = 0.1 eV Comparison of activation energies DFT vs. Matlantis™ Activation energies of methanation reactions for synthesis gas on Co(0001) Effect of promoters on relative activation energy Matlantis CO dissociation (Co+V) C5+ formation scheme 🡪 Activation energy lowered by 55 kJ/mol – 40% 14
15. © Preferred Computational Chemistry, Inc. All Rights Reserved. Battery case: Li diffusion in solid electrolytes https://www.kek.jp/ja/newsroom/2016/06/22/1133/ Root mean square displacement (at 523 K) Diffusion coefficient Activation Energy（meV） Matlantis™ DFT [1] Exp. [2] 230 210 242 [1] Mo et al. Chem. Mater. (2012) 24, 15-17 [2] Y. Kato et al. Nat. Energy 1, 16030. https://matlantis.com/en/cases/calculation004/ Matlantis LGPS: well-known Li-ion conductor Ion diffusion Aim: Predict the diffusion coefficient of lithium ions in LGPS Approach: MD simulation in NVT ensemble Result: Reproduced the characteristic features of Li diffusion. The results are almost identical to that of DFT Calculation time: 2-3 days for the Arrhenius plot (vs. around a year by first-principles molecular dynamics) 15
16. © Preferred Computational Chemistry, Inc. All Rights Reserved. Lubricant case: In silico screening of base oil molecular structures Objectives and Targets of the Study Dr Tasuku Onodera 16
17. © Preferred Computational Chemistry, Inc. All Rights Reserved. Lubricant case: In silico screening of base oil molecular structures Mechanism and Base Oil Selection Guidelines Dr Tasuku Onodera Good Bad 17
18. © Preferred Computational Chemistry, Inc. All Rights Reserved. Lubricant case: In silico screening of base oil molecular structures Base oil screening methods Step 2 Dr Tasuku Onodera 18
19. © Preferred Computational Chemistry, Inc. All Rights Reserved. Lubricant case: In silico screening of base oil molecular structures Characterization of oil film structure (step 2) Dr Tasuku Onodera 19
20. © Preferred Computational Chemistry, Inc. All Rights Reserved. Lubricant case: In silico screening of base oil molecular structures Characterization of oil film structure (step 2) Dr Tasuku Onodera 20
21. © Preferred Computational Chemistry, Inc. All Rights Reserved. Lubricant case: In silico screening of base oil molecular structures Mapping and Rapid Screening Dr Tasuku Onodera 21 Teacher data Predicted data Available compounds
22. © Preferred Computational Chemistry, Inc. All Rights Reserved. Lubricant case: In silico screening of base oil molecular structures Proposal of base oils for better processing Dr Tasuku Onodera 22 Teacher data Predicted data Available compounds
23. © Preferred Computational Chemistry, Inc. All Rights Reserved. Matlantis™ > dramatically accelerates mechanisms elucidation > enables large-scale screening for fast chemicals and materials discovery > facilitates the digitalization of R&D departments Wrap-up Recent updates… o Contract research activities for non-expert customers o “Light-PFP” feature allowing users to study up to 1 000 000 atoms o 13 billion-parameter pre-trained Large Language Model (future co-pilot) https://matlantis.com 23
24. 24 Visit us during ACHEMA 2024: Hall 11.0 Stand F77 Q&A time