CMSP Seminar Wednesday 6 November at 11am - Bingqing Cheng

[CM Section]

CMSP Seminar

Wednesday 6 November at 11:00 a.m.
Luigi Stasi Seminar Room, Leonardo building

Bingqing CHENG
Trinity College, the University of Cambridge, U.K.

'Ab initio Thermodynamics with the help of Machine Learning'

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 heat capacity, 
density, and chemical
potential.

In this talk, I will discuss how to enable such predictions by combining 
advanced free energy
methods 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.

References:
[1] B. Cheng, J. Behler, M. Ceriotti, Journal of Physical Chemistry 
Letters 7 (2016) 2210-2215.
[2] B. Cheng, M. Ceriotti, Physical Review B 97 (2018) 054102.
[3] B. Cheng, E. A. Engel, J. Behler, C. Dellago, M. Ceriotti, 
Proceedings of the National
Academy of Sciences 116 (2019) 1110-1115.
[4] B. Cheng, G. Mazzola, M. Ceriotti, (2019) arXiv:1906.03341

Everyone is welcome to attend.

Indico web page: http://indico.ictp.it/event/9201/