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• W3204673980 startingPage "100739" @default.
• W3204673980 abstract "Porous crystalline materials, such as zeolites and metal-organic frameworks (MOFs), have shown great promises with superior separation, catalysis and upgrading performances in many areas of energy, the environment and health. However, the discovery of new zeolites and MOFs with desired properties is a complex process that often involves trial-and-error experimental/computational approaches. Computational discovery of new materials often involves learning and optimizing more than one objective such as stability, system equilibrium and efficiency. The knowledge gaps between computational prediction and actual synthesis of materials present hurdles to scientific discovery. Advances are underway in machine learning (ML)—in particular, deep reinforcement learning (DRL)—to address these challenges. The goal of this article is to systematically present the key elements and sketch the first steps towards ML-based design and discovery of new zeolites and similar crystalline materials." @default.
• W3204673980 created "2021-10-11" @default.
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• W3204673980 date "2022-03-01" @default.
• W3204673980 modified "2023-10-16" @default.
• W3204673980 title "Machine learning for the design and discovery of zeolites and porous crystalline materials" @default.
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• W3204673980 doi "https://doi.org/10.1016/j.coche.2021.100739" @default.
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