Erratum for a new self‐consistent empirical potential model for multicomponent borate and borosilicate glasses

Bertani, Marco; Pallini, Annalisa; Lodesani, Federica; Cocchi, Marina; Menziani, Maria Cristina; Pedone, Alfonso

doi:10.1111/jace.19158

Cited by 1 publication

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“…The structure (total distribution functions, bond angle distributions, and Q n distributions) and properties (densities, coefficient of thermal expansion, vibrational density of states, and 29 Si, 23 Na, and 17 O MAS and MQMAS NMR spectra) of different glasses and crystals in the compositional domain have been compared with experimental and aiMD data as well as with the results produced by well-established empirical potentials, in particular, with the BMP, ,, SHIK, , and core–shell (CS) potentials. In the latter cases, the simulations were performed on the same simulation boxes of the MLP (700 atoms) following the computational procedures reported in refs ,, .…”

Section: Methodsmentioning

confidence: 99%

Accurate and Transferable Machine Learning Potential for Molecular Dynamics Simulation of Sodium Silicate Glasses

Bertani,

Charpentier,

Faglioni

et al. 2024

J. Chem. Theory Comput.

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An accurate and transferable machine learning (ML) potential for the simulation of binary sodium silicate glasses over a wide range of compositions (from 0 to 50% Na2O) was developed. The potential energy surface is approximated by the sum of atomic energy contributions mapped by a neural network algorithm from the local geometry comprising information on atomic distances and angles with neighboring atoms using the DeePMD code [WangH. Wang, H. Comput. Phys. Commun.2018228178184]. Our model was trained on a large data set of total energies and atomic forces computed at the density functional theory level on structures extracted from classical molecular dynamics (MD) simulations performed at several temperatures from 300 to 3000 K. This allows for the generation of a robust and transferable ML potential applicable over the full compositional range of glass formability at different temperatures that outperforms the empirical potentials available in the literature in reproducing structures and properties such as bond angle distribution, total distribution functions, and vibrational density of state. The generality of the approach enables the future training of a potential with other or more elements allowing for simulations of structures, properties, and behavior of ternary and multicomponent oxide glasses with nearly ab initio accuracy at a fraction of the computational cost.

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