Transferability of machine learning potentials: Protonated water neural network potential applied to the protonated water hexamer
- 1 February 2021
- journal article
- research article
- Published by AIP Publishing in The Journal of Chemical Physics
- Vol. 154 (5), 051101
- https://doi.org/10.1063/5.0035438
Abstract
A previously published neural network potential for the description of protonated water clusters up to the protonated water tetramer, H+(H2O)4, at an essentially converged coupled cluster accuracy [C. Schran, J. Behler, and D. Marx, J. Chem. Theory Comput. 16, 88 (2020)] is applied to the protonated water hexamer, H+(H2O)6—a system that the neural network has never seen before. Although being in the extrapolation regime, it is shown that the potential not only allows for quantum simulations from ultra-low temperatures ∼1 K up to 300 K but is also able to describe the new system very accurately compared to explicit coupled cluster calculations. This transferability of the model is rationalized by the similarity of the atomic environments encountered for the larger cluster compared to the environments in the training set of the model. Compared to the interpolation regime, the quality of the model is reduced by roughly one order of magnitude, but most of the difference to the coupled cluster reference comes from global shifts of the potential energy surface, while local energy fluctuations are well recovered. These results suggest that the application of neural network potentials in extrapolation regimes can provide useful results and might be more general than usually thought.Keywords
Funding Information
- Deutsche Forschungsgemeinschaft (EXC 2033)
- Alexander von Humboldt-Stiftung
This publication has 43 references indexed in Scilit:
- Neural Network Potential Energy Surfaces for Small Molecules and ReactionsChemical Reviews, 2020
- Machine learning for interatomic potential modelsThe Journal of Chemical Physics, 2020
- Machine Learning Interatomic Potentials as Emerging Tools for Materials ScienceAdvanced Materials, 2019
- Machine learning for molecular and materials scienceNature, 2018
- Machine learning unifies the modeling of materials and moleculesScience Advances, 2017
- First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed SystemsAngewandte Chemie, 2017
- Perspective: Machine learning potentials for atomistic simulationsThe Journal of Chemical Physics, 2016
- Amp: A modular approach to machine learning in atomistic simulationsComputer Physics Communications, 2016
- Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural networkPhysical Review B, 2015
- Generalized Neural-Network Representation of High-Dimensional Potential-Energy SurfacesPhysical Review Letters, 2007