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Title: Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation

Journal Article · · ACS Catalysis
ORCiD logo [1];  [2]; ORCiD logo [1]; ORCiD logo [1];  [2]; ORCiD logo [1]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
  2. Clemson Univ., Clemson, SC (United States)
+ Show Author Affiliations

Metal–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.

Research Organization:
Energy Frontier Research Centers (EFRC) (United States). Energy Frontier Research Center for Inorganometallic Catalyst Design (ICDC); Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
Grant/Contract Number:
SC0012702
OSTI ID:
1560618
Journal Information:
ACS Catalysis, Vol. 9, Issue 9; ISSN 2155-5435
Publisher:
American Chemical Society (ACS)Copyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 54 works
Citation information provided by
Web of Science

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Reaction mechanisms at the homogeneous–heterogeneous frontier: insights from first-principles studies on ligand-decorated metal nanoparticles journal January 2019
In silico high throughput screening of bimetallic and single atom alloys using machine learning and ab initio microkinetic modelling journal January 2020

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
metal−oxo species
machine learning
density functional theory
spin-state-dependent reactivity
transition metal catalysis
artificial neural networks