Machine learning-aided first-principles calculations of redox potentials

doi:10.1038/s41524-024-01295-6

Machine learning-aided first-principles calculations of redox potentials

2024-05-20

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DOI：10.1038/s41524-024-01295-6

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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 Delta-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.

Keywords:

DENSITY-FUNCTIONAL THEORY

MOLECULAR-DYNAMICS SIMULATIONS

TOTAL-ENERGY CALCULATIONS

OXYGEN REDUCTION

HYDRATION SHELLS

CHEMISTRY

WATER

ION

INTERFACE

EXCHANGE

Machine learning-aided first-principles calculations of redox potentials

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