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Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation
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posted on 2019-08-20, 18:04 authored by Aditya Nandy, Jiazhou Zhu, Jon Paul Janet, Chenru Duan, Rachel B. Getman, Heather J. KulikMetal–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.
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property spacereactive propertiesoxo formation energy predictionconnectivity-only featuresspin-state dependencetrain machine-learningfirst-row metalsmachine Learning AcceleratesML modelsDesign Rulestest dataoxidative stabilityNew metalANNcounterintuitive oxo formation energiescatalyst spacewater splittingligand orientationsML-model-driven discoveryoxidation statestransition metaloxidation state combinationsDFTfeature importancecatalyst design rulesapproach doublesoxo formation energeticskernel ridge regressionligand properties
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