Computing thermodynamic and transport properties using machine learning potentials

Bingqing Cheng
University of Cambridge

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 [1], 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 [2], 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 the computation of thermodynamic properties, I will talk about transport properties: Ref [3] proposed a method to compute the heat conductivities of liquid 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 [4].

References

[1] Bingqing Cheng, Edgar A Engel, Jörg Behler, Christoph Dellago, Michele Ceriotti. (2019) ab initio thermodynamics of liquid and solid water. Proceedings of the National Academy of Sciences, 116 (4), 1110-1115.

[2] Bingqing Cheng, Guglielmo Mazzola, Chris J. Pickard, Michele Ceriotti. (2020) Evidence for supercritical behaviour of high-pressure liquid hydrogen. Nature, 585, 217–220

[3] Bingqing Cheng, Daan Frenkel. (2020) Computing the Heat Conductivity of Fluids from Density Fluctuations. Physical Review Letters, 125, 130602

[4] Bartomeu Monserrat, Jan Gerit Brandenburg, Edgar A. Engel, Bingqing Cheng. (2020) Liquid water contains the building blocks of diverse ice phases. Nature Communications 11.1: 1-8.

Speaker Bio
I am a Departmental Early Career Fellow at the Computer Science Department, University of Cambridge. My research uses computer simulations to understand and predict material properties, with a particular focus on exploiting machine-learning methods to extend the scope of atomistic simulations. I did my PhD in Michele Ceriotti's group, at EPFL.

Computing thermodynamic and transport properties using machine learning potentials

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