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Accurate and transferable neural network potentials for reactive simulations of siliceous zeolites

Chemistry

Accurate and transferable neural network potentials for reactive simulations of siliceous zeolites

A. Erlebach

This groundbreaking research by A. Erlebach and colleagues introduces reactive SchNet neural network potentials for enhanced modeling of silica's potential energy surfaces. With improved accuracy in predicting structural and vibrational properties, this study paves the way for innovative simulations in glass melting and zeolite amorphization.

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Playback language: English
Abstract
This work presents the training of reactive SchNet neural network potentials (NNPs) for accurate and general potential energy surface (PES) modeling of silica, encompassing the structural diversity of zeolites across a wide density range. The trained NNPs facilitate the reoptimization of the Deem and IZA databases, enabling the prediction of structure and vibrational properties, and simulations of reactive phase transformations like glass melting and zeolite amorphization. The NNPs achieve significantly improved accuracy compared to other PES approximations.
Publisher
npj Computational Materials
Published On
Jan 31, 2022
Authors
A. Erlebach
Tags
reactive SchNet
neural network potentials
potential energy surface
silica
zeolites
glass melting
vibrational properties

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