Machine learning-aided first-principles calculations of redox potentials

Autor(en)

Ryosuke Jinnouchi, Ferenc Karsai, Georg Kresse

Abstrakt

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(en)

Computergestützte Materialphysik

Externe Organisation(en)

VASP Software GmbH, Toyota Central R&D Labs., Inc.

Journal

npj Computational Materials

Band

10

Anzahl der Seiten

11

ISSN

2096-5001

DOI

https://doi.org/10.48550/arXiv.2309.13217

Publikationsdatum

05-2024

Peer-reviewed

Ja

ÖFOS 2012

104022 Theoretische Chemie, 104005 Elektrochemie, 103043 Computational Physics

ASJC Scopus Sachgebiete

Modelling and Simulation, Allgemeine Materialwissenschaften, Mechanics of Materials, Computer Science Applications

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/60e6b754-22cb-47b6-bdb3-20513352c3ca