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

Learn more Watch now 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



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.

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Chair - Gábor Csányi 

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.




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