革新知能統合研究センター

EPFL CIS and RIKEN AIP started a seminar, titled “EPFL CIS – RIKEN AIP Joint Seminar series” from October, 2021.

EPFL is located in Switzerland and is one of the most vibrant and cosmopolitan science and technology institutions. EPFL has both a Swiss and international vocation and focuses on three missions: teaching, research and innovation.

The Center for Intelligent Systems (CIS) at EPFL, a joint initiative of the schools ENAC, IC, SB, STI and SV seeks to advance research and practice in the strategic field of intelligent systems.

RIKEN is Japan’s largest comprehensive research institution renowned for high-quality research in a diverse range of scientific disciplines.

RIKEN Center for Advanced Intelligence Project (AIP) houses more than 40 research teams ranging from fundamentals of machine learning and optimization, applications in medicine, materials, and disaster, to analysis of ethics and social impact of artificial intelligence.

【The 7th Seminar】

Date and Time: January 19th 6:00pm – 7:00pm(JST)
10:00am-11:00pm(CET)
Venue:Zoom webinar

Language: English

Speaker: Michele Ceriotti, EPFL CIS

Title: Representing atoms clouds: the foundations of atomic-scale machine learning

Abstract:
Simulations of matter at the atomic scale are precious to provide a mechanistic understanding of chemical processes, and to design molecules and materials with predictive accuracy. As with many fields of science, machine learning have become an essential part of the modeling toolbox, with many frameworks having become well-established, and many more being developed in new research directions.
Lately, much effort has been dedicated to rationalizing the relationship between models of atomic-scale properties and fundamental physical principles, such as symmetry, locality, and hierarchical decompositions of the interactions between atoms.
I will present an overview of the latest developments, focusing in particular on the problem of representing an atomic structure in a concise, yet complete, fashion. I will discuss a few examples of the implications of these fundamental findings, for both chemical machine learning and in general for problems that require a description of three-dimensional objects in terms of point clouds.

All participants are required to agree with the AIP Seminar Series Code of Conduct.
Please see the URL below.
https://aip.riken.jp/event-list/termsofparticipation/?lang=en

RIKEN AIP will expect adherence to this code throughout the event. We expect cooperation from all participants to help ensure a safe environment for everybody.