University of Cambridge > > MSM-AIMR Joint Online Workshop 2020  > Ab initio thermodynamics with the help of machine learning

Ab initio thermodynamics with the help of machine learning

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Joseph Nelson.

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 [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.


[1] B. Cheng, E. A. Engel, J. Behler, C. Dellago, M. Ceriotti, Proceedings of the National Academy of Sciences 116 (2019) 1110-1115.

[2] B. Cheng, G. Mazzola, C. J. Pickard, M. Ceriotti, Nature (in press)

This talk will be held online using Zoom. Please register your email address here to receive Zoom links via email.

Workshop website:

This talk is part of the MSM-AIMR Joint Online Workshop 2020 series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


© 2006-2024, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity