{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T14:17:02Z","timestamp":1788617822301,"version":"build-2803163510"},"reference-count":94,"publisher":"Wiley","issue":"31","license":[{"start":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T00:00:00Z","timestamp":1763683200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["24K01240"],"award-info":[{"award-number":["24K01240"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Comput Chem"],"published-print":{"date-parts":[[2025,12,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Understanding molecular adsorption and desorption on metal surfaces is crucial for heterogeneous catalysis and surface science. In this work, we systematically evaluate the performance of various density functional theory (DFT) exchange\u2013correlation functionals\u2014PBE, PBE\u2010D3, revPBE\u2010D3, optB88\u2010vdW, BEEF\u2010vdW, and SCAN+rVV10\u2014and a neural network potential for modeling molecular binding relevant to temperature programmed desorption (TPD). Using well\u2010characterized systems, we compare computed bulk lattice constants, surface energies, and binding energies of representative adsorbates (CO, CO\n                    <jats:sub>2<\/jats:sub>\n                    , methanol, and benzene) on transition metal surfaces (Ni, Cu, Ru, Rh, Pd, Ag, Pt, Au) against experimental TPD data. Our results reveal that dispersion\u2010corrected functionals like PBE\u2010D3 and SCAN+rVV10 yield accurate bulk lattice constants, while BEEF\u2010vdW tends to overestimate them. optB88\u2010vdW and SCAN+rVV10 accurately reproduce surface energies, while PBE\u2010D3 and revPBE\u2010D3 often overestimate them. On the other hand, BEEF\u2010vdW provides better agreement for binding energies but at the cost of less accurate bulk properties. We further demonstrate how machine\u2010learned potentials can efficiently reproduce DFT\u2010level energetics and enable molecular dynamics simulations to extract more realistic kinetic parameters, including pre\u2010exponential factors and potential of mean force (PMF) profiles. This study demonstrates the accuracy of modeling molecular adsorption using DFT and neural network potentials, elucidating the trade\u2010offs associated with functional selection in surface science and offering practical guidance for choosing appropriate computational methods for accurately simulating desorption processes.\n                  <\/jats:p>","DOI":"10.1002\/jcc.70279","type":"journal-article","created":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T17:09:23Z","timestamp":1763744963000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Computational Evaluation of Molecular Binding on Metal Surfaces in Temperature Programmed Desorption: Accuracy of\n                    <scp>DFT<\/scp>\n                    Functionals and Neural\u00a0Network 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