【アーカイブ配信】 “Exploring New Frontiers in Materials Discovery: Insights into Amorphous Materials through Atomistic-Level Simulation with Matlantis” イベントレポート

1月15日に開催された本ウェビナーでは、PFCCのシニアアカウントマネージャーであるRudy Coquetより、弊社独自のニューラルネットワーク原子間ポテンシャルであるMatlantisによるブレークスルーについて講演する共に、東京大学の榊間先生より、計算化学を用いたアモルファス誘電体材料の取り扱いに関する最先端の研究についてご講演いただきました。

当日の資料および動画を公開いたしますので、ウェビナーにご参加いただけなかった方もぜひご覧いただき、研究の最新知見をお役立てください。尚、内容は全て英語となっておりますのでご了承ください。

Contents

Talk 1

Title

Revolutionizing Material and Chemical Discovery with Matlantis: The Cutting-Edge Atomistic Simulation Platform

Speaker

Dr. Coquet, a senior account manager in Preferred Computational Chemistry

Abstract

Preferred Computational Chemistry is proud to introduce Matlantis, our advanced atomistic simulation platform tailored for researchers and engineers seeking breakthroughs in materials and chemical discovery. Matlantis combines cutting-edge machine learning algorithms with the computational rigor of quantum simulations, enabling high-speed and accurate modeling of complex atomic interactions. Leveraging cloud-based technology, it provides unparalleled scalability and user accessibility, empowering innovation across fields such as materials science, catalysis, and nanotechnology. This presentation will explore the core functionalities of Matlantis, demonstrate its application versatility, and showcase success stories that highlight its impact on accelerating research and development.

Talk 2

Title

Exploration of the Composition-Structure-Property Relations of Amorphous Materials with PFP: Applications to elastic properties of SiOC and SiON systems

Speaker

Dr. Sakakima, an assistant professor in the University of Tokyo

Abstract

Revealing the relations between the composition, structure, and resulting properties of amorphous materials is one of the major challenges where the contribution of computational material science is anticipated. However, accurately analyzing the various compositions of these disordered systems has been challenging. This is because the accuracy and computational costs of classical and ab initio molecular dynamics simulations have competing advantages and disadvantages. This long-standing challenge may be overcome by the universal neural network interatomic potentials, such as PFP. In this talk, we focus on amorphous dielectric materials, particularly carbon- or nitrogen-added amorphous silicon oxides (a-SiOC or a-SiON), both of which are necessary for advanced electronic devices. Using PFP, the relations between composition, structure, and elastic properties are examined. The structural and elastic properties of a-SiOC and a-SiON are examined in relation to changes in carbon and nitrogen content, as well as various O-rich and O-poor situations.

Speakers’ Information

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