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• W3131437635 abstract "Abstract The computational discovery and design of zeolites is a crucial part of the chemical industry. Finding highly accurate while computational feasible protocol for identification of hypothetical siliceous frameworks that could be targeted experimentally is a great challenge. To tackle this challenge, we trained neural network potentials (NNP) with the SchNet architecture on a structurally diverse database of density functional theory (DFT) data. This database was iteratively extended by active learning to cover not only low-energy equilibrium configurations but also high-energy transition states. We demonstrate that the resulting reactive NNPs retain DFT accuracy for thermodynamic stabilities, vibrational properties, as well as reactive and non-reactive phase transformations. As a showcase, we screened an existing zeolite database and revealed >20k additional hypothetical frameworks in the thermodynamically accessible range of zeolite synthesis. Hence, our NNPs are expected to be essential for future high-throughput studies on the structure and reactivity of siliceous zeolites." @default.
• W3131437635 created "2021-03-01" @default.
• W3131437635 creator A5028692913 @default.
• W3131437635 creator A5056495241 @default.
• W3131437635 creator A5073356801 @default.
• W3131437635 date "2022-08-19" @default.
• W3131437635 modified "2023-10-14" @default.
• W3131437635 title "Accurate large-scale simulations of siliceous zeolites by neural network potentials" @default.
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