Neural Network potential for Si by Gupta (2026) N=1 (in series of 3) v000

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Authors: Amit Gupta
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Abstract

This is a general purpose potential for solid state Si. It uses Behler Parrinello symmetry functions as descriptors, has 7 layers (including the output layer), and uses tanh activation functions. The potential was trained on a modified GAP PRX Si dataset, where all the liquid Si configurations were removed to ensure better model performance for the solid state Si. The number `N1` indicates that this is the first potential in the series of 3 identically trained potentials with the same dataset but with different initial weights.

Fields of Study & Subjects

Machine Learning in Materials Science Advanced Chemical Physics Studies Neural Networks and Applications

Publication details

Journal / source
Open Knowledgebase of Interatomic Models (OpenKIM)
Publication date
1 January 2026