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dc.contributor.authorNandy, Aditya
dc.contributor.authorZhu, Jiazhou
dc.contributor.authorJanet, Jon Paul
dc.contributor.authorDuan, Chenru
dc.contributor.authorGetman, Rachel B.
dc.contributor.authorKulik, Heather Janine
dc.date.accessioned2019-09-23T18:30:58Z
dc.date.available2019-09-23T18:30:58Z
dc.date.issued2019-07
dc.date.submitted2019-06
dc.identifier.issn2155-5435
dc.identifier.issn2155-5435
dc.identifier.urihttps://hdl.handle.net/1721.1/122278
dc.description.abstractMetal-oxo moieties are important catalytic intermediates in the selective partial oxidation of hydrocarbons and in water splitting. Stable metal-oxo species have reactive properties that vary depending on the spin state of the metal, complicating the development of structure-property relationships. To overcome these challenges, we train machine-learning (ML) models capable of predicting metal-oxo formation energies across a range of first-row metals, oxidation states, and spin states. Using connectivity-only features tailored for inorganic chemistry as inputs to kernel ridge regression or artificial neural network (ANN) ML models, we achieve good mean absolute errors (4-5 kcal/mol) on set-aside test data across a range of ligand orientations. Analysis of feature importance for oxo formation energy prediction reveals the dominance of nonlocal, electronic ligand properties in contrast to other transition metal complex properties (e.g., spin-state or ionization potential). We enumerate the theoretical catalyst space with an ANN, revealing expected trends in oxo formation energetics, such as destabilization of the metal-oxo species with increasing d-filling, as well as exceptions, such as weak correlations with indicators of oxidative stability of the metal in the resting state or unexpected spin-state dependence in reactivity. We carry out uncertainty-aware evolutionary optimization using the ANN to explore a > 37 000 candidate catalyst space. New metal and oxidation state combinations are uncovered and validated with density functional theory (DFT), including counterintuitive oxo formation energies for oxidatively stable complexes. This approach doubles the density of confirmed DFT leads in originally sparsely populated regions of property space, highlighting the potential of ML-model-driven discovery to uncover catalyst design rules and exceptions. Keywords: metal-oxo species; machine learning; density functional theory; spin-state-dependent reactivity; transition metal catalysisen_US
dc.language.isoen
dc.publisherAmerican Chemical Society (ACS)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1021/acscatal.9b02165en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.sourceACSen_US
dc.titleMachine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formationen_US
dc.typeArticleen_US
dc.identifier.citationNandy, Aditya et al. "Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation." ACS Catalysis 9, 9 (July 2019): 8243-8255 © 2019 American Chemical Societyen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Chemistryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Chemical Engineeringen_US
dc.relation.journalACS Catalysisen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2019-09-20T13:55:21Z
dspace.date.submission2019-09-20T13:55:24Z
mit.journal.volume9en_US
mit.journal.issue9en_US


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