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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Publisher
Bristol: IOP Publishing
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Language
English
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Publisher
Bristol: IOP Publishing
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Contents
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 reconstructions and th...
Alternative Titles
Full title
Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy
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TN_cdi_crossref_primary_10_1088_2632_2153_ac191c
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_crossref_primary_10_1088_2632_2153_ac191c
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ISSN
2632-2153
E-ISSN
2632-2153
DOI
10.1088/2632-2153/ac191c
