University of Cambridge > > Theory - Chemistry Research Interest Group > 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 Lisa Masters.

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 seminar, I will discuss how to enable such predictions by marrying 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 stablities of different phases of water.

This talk is part of the Theory - Chemistry Research Interest Group 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