Machine learning-aided first-principles calculations of redox potentials
- Author(s)
- Ryosuke Jinnouchi, Ferenc Karsai, Georg Kresse
- Abstract
We present a method combining first-principles calculations and machine learning to predict the redox potentials of half-cell reactions on the absolute scale. By applying machine learning force fields for thermodynamic integration from the oxidized to the reduced state, we achieve efficient statistical sampling over a broad phase space. Furthermore, through thermodynamic integration from machine learning force fields to potentials of semi-local functionals, and from semi-local functionals to hybrid functionals using Δ-machine learning, we refine the free energy with high precision step-by-step. Utilizing a hybrid functional that includes 25% exact exchange (PBE0), this method predicts the redox potentials of the three redox couples, Fe3+/Fe2+, Cu2+/Cu+, and Ag2+/Ag+, to be 0.92, 0.26, and 1.99 V, respectively. These predictions are in good agreement with the best experimental estimates (0.77, 0.15, 1.98 V). This work demonstrates that machine-learned surrogate models provide a flexible framework for refining the accuracy of free energy from coarse approximation methods to precise electronic structure calculations, while also facilitating sufficient statistical sampling.
- Organisation(s)
- Computational Materials Physics
- External organisation(s)
- VASP Software GmbH, Toyota Central R&D Labs., Inc.
- Journal
- npj Computational Materials
- Volume
- 10
- No. of pages
- 11
- ISSN
- 2096-5001
- DOI
- https://doi.org/10.48550/arXiv.2309.13217
- Publication date
- 05-2024
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 104022 Theoretical chemistry, 104005 Electrochemistry, 103043 Computational physics
- ASJC Scopus subject areas
- Modelling and Simulation, Materials Science(all), Mechanics of Materials, Computer Science Applications
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/machine-learningaided-firstprinciples-calculations-of-redox-potentials(60e6b754-22cb-47b6-bdb3-20513352c3ca).html