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• W2913902213 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." @default.
• W2913902213 created "2019-02-21" @default.
• W2913902213 creator A5009084547 @default.
• W2913902213 creator A5051587395 @default.
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• W2913902213 date "2019-02-07" @default.
• W2913902213 modified "2023-10-16" @default.
• W2913902213 title "Machine-learning approach to the design of OSDAs for zeolite beta" @default.
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• W2913902213 doi "https://doi.org/10.1073/pnas.1818763116" @default.
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