SISSA Women in Physics colloquium

[De Comelli Marina]

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Dear all,

You are kindly invited to the first of SISSA Women in Physics (SWP) 
colloquia.
It will be held on ***Friday 26th at 15:30***, on Zoom.

Speaker: Dr. Bingqing Cheng
Title: Predicting material properties with the help of machine learning
Affiliation: Department of Computer Science and Technology, University 
of Cambridge

WEBINAR via the platform ZOOM.
Join Zoom Meeting: 
https://sissa-it.zoom.us/j/81509202633?pwd=UmJ2SGJRVmtmcnI5SjBBaHBjYXp0Zz09 
<https://sissa-it.zoom.us/j/81509202633?pwd=UmJ2SGJRVmtmcnI5SjBBaHBjYXp0Zz09>
Meeting ID: 815 0920 2633
Passcode: 337842


Abstract:
A central goal of computational physics and chemistry is to predict 
material properties using first-principles methods based on the 
fundamental laws of quantum mechanics. However, the high computational 
costs of these methods typically prevent rigorous predictions of 
macroscopic quantities at finite temperatures, such as chemical 
potential, heat capacity and thermal conductivity.
In this talk, I will first discuss how to enable such predictions by 
combining advanced statistical mechanics with data-driven machine 
learning interatomic potentials. As an example, for the omnipresent and 
technologically essential system of water, a first-principles 
thermodynamic description not only leads to excellent agreement with 
experiments, but also reveals the crucial role of nuclear quantum 
fluctuations in modulating the thermodynamic stabilities of different 
phases of water. As another example, we simulated the high-pressure 
hydrogen system with converged system size and simulation length, and 
found, contrary to established beliefs, supercritical behaviour of 
liquid hydrogen above the melting line. Besides thermodynamic 
properties, I will talk about how to compute the heat conductivities of 
liquids just from equilibrium molecular dynamics trajectories. During 
the second part of the talk, I will rationalize why machine learning 
potentials work at all, and in particular, the locality argument. I'll 
show that a machine learning potential trained on liquid water alone can 
predict the properties of diverse ice phases, because all the local 
environments characterising the ice phases are found in liquid water.