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Ab initio thermodynamics with the help of machine learning

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If you have a question about this talk, please contact Katarzyna Macieszczak.

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. I will show that, 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.


[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.

This talk is part of the Theory of Condensed Matter series.

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