Bayesian learning of thermodynamic integration and numerical convergence for accurate phase diagrams
- Creators
- Ladygin, V.
- Beniya, I.
- Makarov, E.
- Shapeev, A.
Abstract
Accurate phase diagram calculation from molecular dynamics requires systematic treatment and convergence of statistical averages. In this work we propose a Gaussian process regression based framework for reconstructing the free-energy functions using data of various origins. Our framework allows for propagating statistical uncertainty from finite molecular dynamics trajectories to the phase diagram and automatically performing convergence with respect to simulation parameters. Furthermore, our approach provides a way for automatic optimal sampling in the simulation parameter space based on a Bayesian optimization approach. We validate our methodology by constructing phase diagrams of two model systems, the Lennard-Jones and soft-core potential, and compare the results with the existing studies and our coexistence simulations. Finally, we construct the phase diagram of lithium at temperatures above 300 K and pressures below 30 GPa from a machine-learning potential trained on ab initio data. Our approach performs well when compared to coexistence simulations and experimental results.
Additional Information
© 2021 American Physical Society. (Received 3 May 2021; revised 19 August 2021; accepted 27 August 2021; published 7 September 2021) A.S. thanks Richard Otis (Caltech) for extensive discussions that led to the creation of this work. This work was supported by the Russian Foundation for Basic Research under Grant No. 20-53-12012.Attached Files
Published - PhysRevB.104.104102.pdf
Submitted - 2104.09479.pdf
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Additional details
- Eprint ID
- 111063
- Resolver ID
- CaltechAUTHORS:20210927-213256207
- 20-53-12012
- Russian Foundation for Basic Research
- Created
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2021-09-27Created from EPrint's datestamp field
- Updated
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2021-09-27Created from EPrint's last_modified field