Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary a...
Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy
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United States: IOP Publishing
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English
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United States: IOP Publishing
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Abstract The majority of computational catalyst design focuses on the screening of material components and alloy composition to optimize selectivity and activity for a given reaction. However, predicting the metastability of the alloy catalyst surface at realistic operating conditions requires an extensive sampling of possible surface reconstructio...
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Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy
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TN_cdi_osti_scitechconnect_1979474
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_osti_scitechconnect_1979474
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ISSN
2632-2153
E-ISSN
2632-2153
