Event Report

ウェビナー

Online2024.3.12-14 (JPN)

Report: The future of materials development brought about by the general-purpose atomic-level simulator "Matlantis™" ~ AI-driven ultra-high-speed calculations will change the world of materials development ~

From March 12th to March 14th, 2024, I gave a speech at the "2nd DX Summit for the Petroleum and Chemical Industries," a free live-streamed seminar hosted by Chemical Daily Co., Ltd.
The event was well received by many participants. Thank you very much to everyone who participated.
The presentation materials will be made public, so please take a look.

Title

"The Future of Materials Development Brought About by the General-Purpose Atomic-Level Simulator 'Matlantis™' ~ AI-Driven Ultra-High-Speed Calculations Change the World of Materials Development ~"

Overview of the Webinar

Matlantis is a general-purpose atomic-level simulator that trains a unique neural network with vast amounts of computational data. Since it was launched in Japan as a cloud service in 2021, the number of companies adopting it has steadily increased, and from 2023, the business has been expanding overseas. In this presentation, we provided an overview of Matlantis and introduced our efforts in materials development with examples. We also proposed the future vision of materials development using computational chemistry.

Presentation Materials

Speaker Information

Masateru Kawaguchi

Product Manager, Preferred Computational Chemistry Inc.
Graduated from the Graduate School of Aeronautics and Astronautics at Tohoku University. After graduating, he worked as a software engineer in product development for the manufacturing industry for over 10 years, and then led product development as a development manager at a startup company. In 2018, he joined Preferred Networks, Inc. From 2020, he has been involved in the development of Matlantis, and later served as the manager of the product. He will assume his current position in December 2023.

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[Webinar] Matlantis: Universal Atomistic Simulation for AI-Driven Materials Discovery