MATLANTIS - [On-demand version now available] How to create a general-purpose neural network potential for materials exploration MRS

An on-demand version of the webinar "History towards Universal Neural Network Potential for Material Discovery", co-hosted with the Materials Research Society on October 4, 2023, is now available.
Please be sure to watch it.

Abstract

The rapid advancements of Artificial Intelligence technology have brought about revolutionary changes in materials discovery.

Neural Network Potential (NNP) describes molecular dynamics force field using a neural network, and many physical properties can be simulated using this single neural network. The webinar reviews the history of NNP research to understand how dataset & neural network architecture are improved.

We also describe the effort to develop a universal neural network and introduce the “PreFerred Potential (PFP)” implemented in Matlantis.

Watch the video (external site)

Who should attend

Computational scientists who are facing limitations of their current calculation/simulation approach
Researchers who are interested in learning game-changing atomistic simulation method
Anyone who is interested in knowing about what’s happening at the forefront of materials informatics

Speakers

Kosuke Nakago

Preferred Networks, Inc.

Kosuke Nakago is an Engineer at Preferred Networks, Inc. He received his master degree in theoretical physics from the University of Tokyo in 2014. He worked on deep learning research & development at Preferred Networks, Inc. and developed chainer-chemistry, A Library for Deep Learning in Biology and Chemistry. He is also supporting users to utilize Matlantis for their innovative material discovery at Preferred Computational Chemistry, Inc. His research interest includes deep learning as well as its application to materials science. He also participates data science competition and he is Kaggle competition master and notebooks grandmaster.

Taku Watanabe

Preferred Computational Chemistry Inc.

Taku Watanabe, PhD. is a Lead Researcher at Preferred Computational Chemistry Inc. He earned his Ph.D. in Materials Science and Engineering from the University of Florida and did postdoctoral research in Chemical Engineering at Georgia Institute of Technology. In 2012, he joined Samsung R&D Institute Japan and dedicated his career for all-solid-state battery research for nearly eight years. His current research interest extends to battery materials, nanoporous solids, surface science, and the application of machine learning technology to computational chemistry in general.