[Webinar Archive] “Exploring New Frontiers in Materials Discovery: Insights into Amorphous Materials through Atomistic-Level Simulation with Matlantis”
At the webinar held on January 14th, Rudy Coquet, Senior Account Manager at PFCC, gave a lecture on breakthroughs achieved using our proprietary neural network interatomic potential, Matlantis. Additionally, Dr. Sakakima, an assistant professor from the University of Tokyo, presented his cutting-edge research on the treatment of amorphous dielectric materials using computational chemistry.
We are pleased to share the slides and video from the day, so those who were unable to attend the webinar can also benefit from the latest research insights.
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
Rudy Coquet
Senior Account Manager at Preferred Computational Chemistry Inc. , he received his PhD in Chemistry from Cardiff University, UK in 2005 and his MBA from HEC Paris, France in 2013.
Hiroki Sakakima
H. Sakakima completed his doctoral studies in mechanical engineering at the Graduate School of Engineering at the University of Tokyo in 2020, and has been an assistant professor there since April 2020. He specializes in computational simulations for strength and mechanics of materials based on classical molecular dynamics and first-principles calculations. Currently, he is mainly working on materials for semiconductor device applications.
