Interpretable Machine Learning of Chemical Bonding at Solid Surfaces

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dc.contributor.authorOmidvar, Noushinen
dc.contributor.authorPillai, Hemanth Somarajanen
dc.contributor.authorWang, Shih-Hanen
dc.contributor.authorMou, Tianyouen
dc.contributor.authorWang, Siwenen
dc.contributor.authorAthawale, Andyen
dc.contributor.authorAchenie, Luke E. K.en
dc.contributor.authorXin, Hongliangen
dc.date.accessioned2022-02-13T00:47:42Zen
dc.date.available2022-02-13T00:47:42Zen
dc.date.issued2021-11-25en
dc.date.updated2022-02-13T00:47:33Zen
dc.description.abstractUnderstanding the nature of chemical bonding and its variation in strength across physically tunable factors is important for the development of novel catalytic materials. One way to speed up this process is to employ machine learning (ML) algorithms with online data repositories curated from high-throughput experiments or quantum-chemical simulations. Despite the reasonable predictive performance of ML models for predicting reactivity properties of solid surfaces, the ever-growing complexity of modern algorithms, e.g., deep learning, makes them black boxes with little to no explanation. In this Perspective, we discuss recent advances of interpretable ML for opening up these black boxes from the standpoints of feature engineering, algorithm development, and post hoc analysis. We underline the pivotal role of interpretability as the foundation of next-generation ML algorithms and emerging AI platforms for driving discoveries across scientific disciplines.en
dc.description.versionAccepted versionen
dc.format.extentPages 11476-11487en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1021/acs.jpclett.1c03291en
dc.identifier.eissn1948-7185en
dc.identifier.issn1948-7185en
dc.identifier.issue46en
dc.identifier.orcidAchenie, Luke [0000-0001-9850-5346]en
dc.identifier.orcidXin, Hongliang [0000-0001-9344-1697]en
dc.identifier.pmid34793170en
dc.identifier.urihttp://hdl.handle.net/10919/108327en
dc.identifier.volume12en
dc.language.isoenen
dc.publisherAmerican Chemical Societyen
dc.relation.urihttps://www.ncbi.nlm.nih.gov/pubmed/34793170en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject02 Physical Sciencesen
dc.subject03 Chemical Sciencesen
dc.titleInterpretable Machine Learning of Chemical Bonding at Solid Surfacesen
dc.title.serialJournal of Physical Chemistry Lettersen
dc.typeArticleen
dc.type.dcmitypeTexten
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Chemical Engineeringen
pubs.organisational-group/Virginia Tech/University Research Institutesen
pubs.organisational-group/Virginia Tech/University Research Institutes/Fralin Life Sciencesen
pubs.organisational-group/Virginia Tech/Faculty of Health Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.organisational-group/Virginia Tech/University Research Institutes/Fralin Life Sciences/Durelle Scotten

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