Schienbein, P;
Blumberger, J;
(2024)
Data-Efficient Active Learning for Thermodynamic Integration: Acidity Constants of BiVO4 in Water.
ChemPhysChem
, Article e202400490. 10.1002/cphc.202400490.
(In press).
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Abstract
The protonation state of molecules and surfaces is pivotal in various disciplines, including (electro−)catalysis, geochemistry, biochemistry, and pharmaceutics. Accurately and efficiently determining acidity constants is critical yet challenging, particularly when explicitly considering the electronic structure, thermal fluctuations, anharmonic vibrations, and solvation effects. In this research, we employ thermodynamic integration accelerated by committee Neural Network potentials, training a single machine learning model that accurately describes the relevant protonated, deprotonated, and intermediate states. We investigate two deprotonation reactions at the BiVO4 (010)-water interface, a promising candidate for efficient photocatalytic water splitting. Our results illustrate the convergence of the required ensemble averages over simulation time and of the final acidity constant as a function of the Kirkwood coupling parameter. We demonstrate that simulation times on the order of nanoseconds are required for statistical convergence. This time scale is currently unachievable with explicit ab-initio molecular dynamics simulations at the hybrid DFT level of theory. In contrast, our machine learning workflow only requires a few hundred DFT single point calculations for training and testing. Exploiting the extended time scales accessible, we furthermore asses the effect of commonly applied bias potentials. Thus, our study significantly advances calculating free energy differences with ab-initio accuracy.
| Type: | Article |
|---|---|
| Title: | Data-Efficient Active Learning for Thermodynamic Integration: Acidity Constants of BiVO4 in Water |
| Location: | Germany |
| DOI: | 10.1002/cphc.202400490 |
| Publisher version: | http://dx.doi.org/10.1002/cphc.202400490 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Bismuth vanadate, deprotonation free energy, machine learning potential, solid-liquid water interface, thermodynamic integration |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10201606 |
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