Optimizing the architecture of Behler-Parrinello neural network potentials
- Author(s)
- Lukáš Kývala, Christoph Dellago
- Abstract
The architecture of neural network potentials is typically optimized at the beginning of the training process and remains unchanged throughout. Here, we investigate the accuracy of Behler-Parrinello neural network potentials for varying training set sizes. Using the QM9 and 3BPA datasets, we show that adjusting the network architecture according to the training set size improves the accuracy significantly. We demonstrate that both an insufficient and an excessive number of fitting parameters can have a detrimental impact on the accuracy of the neural network potential. Furthermore, we investigate the influences of descriptor complexity, neural network depth, and activation function on the model’s performance. We find that for the neural network potentials studied here, two hidden layers yield the best accuracy and that unbounded activation functions outperform bounded ones.
- Organisation(s)
- Computational and Soft Matter Physics
- Journal
- Journal of Chemical Physics
- Volume
- 159
- No. of pages
- 8
- ISSN
- 0021-9606
- DOI
- https://doi.org/10.1063/5.0167260
- Publication date
- 09-2023
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 102019 Machine learning, 103043 Computational physics, 103029 Statistical physics
- ASJC Scopus subject areas
- Physics and Astronomy(all), Physical and Theoretical Chemistry
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/optimizing-the-architecture-of-behlerparrinello-neural-network-potentials(a14cd157-f5fc-48bb-9314-543b34703f41).html