Tests of Artificial Neural Network-Based Diabatization Approaches on Simple 1D Models
Martina Ćosićová, Thierry Leininger, René Kalus
TL;DR
This paper tests and improves a machine learning method for chemical simulations using simple models.
Contribution
The study introduces improvements to a neural network-based diabatization method through activation functions and regularization.
Findings
Nonlinear activation functions in the output layer improved performance.
Regularization in the loss function enhanced method accuracy.
Cheap training set extensions significantly improved results.
Abstract
Recently, a novel diabatization scheme has been proposed [ ShuY., ; TruhlarD. G., J. Chem. Theory Comput. 2020, 16, 6456–6464]32886513 10.1021/acs.jctc.0c00623 using artificial neural networks. Most importantly, the method almost exclusively requires the knowledge of adiabatic energies, which are routinely obtained from ab initio calculations. However, many questions related to the favorable performance of the method remain unanswered. In the present paper, some of these questions are considered for selected one-dimensional models with one configurational variable. In particular, various activation functions are tested, including nonlinear ones in the output layer, the effect of the regularization term in the loss function is analyzed, and computationally cheap extensions of training sets are proposed. Significant improvements of the performance of the original method have been…
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Taxonomy
TopicsNeural Networks and Applications