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Machine-learning approach to the design of OSDAs for zeolite beta.

ABSTRACT: We report a machine-learning strategy for design of organic structure directing agents (OSDAs) for zeolite beta. We use machine learning to replace a computationally expensive molecular dynamics evaluation of the stabilization energy of the OSDA inside zeolite beta with a neural network prediction. We train the neural network on 4,781 candidate OSDAs, spanning a range of stabilization energies. We find that the stabilization energies predicted by the neural network are highly correlated with the molecular dynamics computations. We further find that the evolutionary design algorithm samples the space of chemically feasible OSDAs thoroughly. In total, we find 469 OSDAs with verified stabilization energies below -17 kJ/(mol Si), comparable to or better than known OSDAs for zeolite beta, and greatly expanding our previous list of 152 such predicted OSDAs. We expect that these OSDAs will lead to syntheses of zeolite beta.

SUBMITTER: Daeyaert F

PROVIDER: S-EPMC6397530 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Machine-learning approach to the design of OSDAs for zeolite beta.

Daeyaert Frits F Ye Fengdan F Deem Michael W MW

Proceedings of the National Academy of Sciences of the United States of America 20190207 9

We report a machine-learning strategy for design of organic structure directing agents (OSDAs) for zeolite beta. We use machine learning to replace a computationally expensive molecular dynamics evaluation of the stabilization energy of the OSDA inside zeolite beta with a neural network prediction. We train the neural network on 4,781 candidate OSDAs, spanning a range of stabilization energies. We find that the stabilization energies predicted by the neural network are highly correlated with the ...[more]

PMID: 30733290

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