The Alan Turing Institute

Nature Reviews Physics: Machine learning in condensed matter and materials physics

Learn more Add to Calendar 09/28/2022 04:00 PM 09/28/2022 05:10 PM Europe/London Nature Reviews Physics: Machine learning in condensed matter and materials physics Location of the event
Wednesday 28 Sep 2022
Time: 16:00 - 17:10

Event type

Virtual seminar

Audience type

Cross-disciplinary
Free

Introduction

Machine learning methods are now used in the simulation of the building blocks of matter: from the electronic- to the molecular-level structure. These tools have boosted well-known computational methods such as density functional theory or molecular dynamics simulation, and are expected to lead to new physical insights which, in turn, can enable the engineering of exotic new materials.

 

About the event

In this event, we will hear from Professor Eun-Ah Kim and Assistant Professor Michele Ceriotti. 

Professor Eun-Ah Kim - Machine leaning quantum emergence 

Decades of efforts in improving computing power and experimental instrumentation were driven by our desire to better understand the complex problem of quantum emergence. The resulting "data revolution" presents new challenges. I will discuss how these challenges can be embraced and turned into opportunities through machine learning. The scientific questions in the field of electronic quantum matter require fundamentally new approaches to data science for two reasons. Firstly, quantum mechanics restricts our access to information and secondly, inference from data should be subject to fundamental laws of physics. Hence machine learning quantum emergence requires collective wisdom of data science and condensed matter physics. I will review rapidly developing efforts by the community in using machine learning to solve problems and gain new insight. I will then present my group’s results on the machine-learning-based analysis of complex experimental data on quantum matter.

Assistant Professor Michele Ceriotti - Atomic-scale simulations of matter with machine learning 

Machine learning has become an indispensable tool in the simulation of matter at the atomic scale, replacing or supporting electronic structure calculations that are in turn increasingly more accurate and predictive. I will present a brief overview of the key concepts that have emerged as the guiding principles in the effective application of ML to this class of problems. Additionally, I will provide several examples of applications that have been made possible by the synergies between data-driven and physics-based modelling. I will summarise what I see as the most pressing challenges and promising research directions in the field.

Who should attend?

This event is of interest to physicists from all fields who are using or intending to use AI in their research. It is equally relevant to AI scientists who are developing new machine learning tools and who would want to know what types of problems physicists are trying to solve. The talks and the follow-up discussion will be kept non-technical so that the content is accessible to non-specialists. At the end there will be an opportunity to ask questions.

About the event series

Nature Reviews Physics and The Alan Turing Institute are tapping into international and local expertise to try to identify the different classes of problems that machine learning can help physicists solve and the key issues that physicists and machine learning scientists should be aware of. In doing so we are hoping to create a framework to facilitate understanding and the exchange of ideas. Read more about the series and register for future events. 

 

Agenda

Chair - Professor Gábor Csányi

16:00 - 16:05 - Event introduction 

16:05 - 16:25 - 20 minute presentation  

16:25 - 16:45 - 20 minute presentation 

16:45 - 17:05 - 20 minute Q&A session

17:05 - 17:10 - Event summary

Register now

Contact info

If you have any further questions about the event, please contact Zaynab Ismail (Research Project Manager) at [email protected]

Speakers

Organisers

Professor Ben MacArthur

Director of AI for Science and Government, Deputy Programme Director for Health and Medical Sciences, and Turing Fellow

Professor Jonathan Rowe

AI for Science and Government Programme Chair, Programme Director for Data Science for Science, and Turing Fellow