Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models

Schwalbe-Koda, Daniel [Materials Science Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA, Department of Materials Science and Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA, Laboratory for Energy Applications for the Future (LEAF), Lawrence Livermore National Laboratory, Livermore, CA 94550, USA] (ORCID:0000000191760854); Govindarajan, Nitish [Materials Science Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA, School of Chemistry, Chemical Engineering, and Biotechnology, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore, Laboratory for Energy Applications for the Future (LEAF), Lawrence Livermore National Laboratory, Livermore, CA 94550, USA] (ORCID:0000000332275183); Varley, Joel B. [Materials Science Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA, Laboratory for Energy Applications for the Future (LEAF), Lawrence Livermore National Laboratory, Livermore, CA 94550, USA]

doi:10.1039/d4dd00328d

DOE OSTI · 2480604

Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models

Schwalbe-Koda, Daniel [Materials Science Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA, Department of Materials Science and Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA, Laboratory for Energy Applications for the Future (LEAF), Lawrence Livermore National Laboratory, Livermore, CA 94550, USA] (ORCID:0000000191760854)·Govindarajan, Nitish [Materials Science Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA, School of Chemistry, Chemical Engineering, and Biotechnology, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore, Laboratory for Energy Applications for the Future (LEAF), Lawrence Livermore National Laboratory, Livermore, CA 94550, USA] (ORCID:0000000332275183)·Varley, Joel B. [Materials Science Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA, Laboratory for Energy Applications for the Future (LEAF), Lawrence Livermore National Laboratory, Livermore, CA 94550, USA]

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Abstract

A combination of generalization in neural networks and fast data pipelines enables comprehensive sampling coverage and co-adsorption effects in heterogeneous catalyst models.

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Schwalbe-Koda, Daniel [Materials Science Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA, Department of Materials Science and Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA, Laboratory for Energy Applications for the Future (LEAF), Lawrence Livermore National Laboratory, Livermore, CA 94550, USA] (ORCID:0000000191760854), Govindarajan, Nitish [Materials Science Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA, School of Chemistry, Chemical Engineering, and Biotechnology, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore, Laboratory for Energy Applications for the Future (LEAF), Lawrence Livermore National Laboratory, Livermore, CA 94550, USA] (ORCID:0000000332275183), Varley, Joel B. [Materials Science Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA, Laboratory for Energy Applications for the Future (LEAF), Lawrence Livermore National Laboratory, Livermore, CA 94550, USA]. 2025-01-15. Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models. https://doi.org/10.1039/d4dd00328d

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