Revisiting Machine Learning Potentials for Silicate Glasses: The Missing Role of Dispersion Interactions

A Pedone and M Bertani and M Benassi, JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 21, 4769-4778 (2025).

DOI: 10.1021/acs.jctc.5c00218

Machine learning interatomic potentials (MLIPs) offer a promising alternative to traditional force fields and ab initio methods for simulating complex materials such as oxide glasses. In this work, we present the first evaluation of the pretrained MACE (Multi-ACE) model D.P. Kovacs et al., J. Chem. Phys. 159(2023), 044118 for silicate glasses, using sodium silicates as a test case. We compare its performance with a DeePMD-based MLIP specifically trained on sodium silicate compositions M. Bertani et al., J. Chem. Theory Comput. 20(2024), 1358-1370 and assess their accuracy in reproducing structural and dynamical properties. Additionally, we investigate the role of dispersion interactions by incorporating the D3(BJ) correction in both models. Our results show that while MACE accurately reproduces neutron structure factors, pair distribution functions, and SiQn speciation, it performs slightly worst for elastic properties calculations. However, it is suitable for the simulations of sodium silicate glasses. The inclusion of dispersion interactions significantly improves the reproduction of density and elastic properties for both MLIPs, highlighting their critical role in glass modeling. These findings provide insight into the transferability of general MLIPs to disordered systems and emphasize the need for dispersion-aware training data sets in developing accurate force fields for oxide glasses.

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