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

Abstract

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 candidatemore » 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.« less

Authors:
ORCiD logo [1];  [2]; ORCiD logo [1]; ORCiD logo [1];  [2]; ORCiD logo [1]
+ Show Author Affiliations
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
  2. Clemson Univ., Clemson, SC (United States)
Publication Date:
Research Org.:
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 Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1560618
Grant/Contract Number:  
SC0012702
Resource Type:
Accepted Manuscript
Journal Name:
ACS Catalysis
Additional Journal Information:
Journal Volume: 9; Journal Issue: 9; Journal ID: ISSN 2155-5435
Publisher:
American Chemical Society (ACS)
Country of Publication:
United States
Language:
English
Subject:
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

Citation Formats

Nandy, Aditya, Zhu, Jiazhou, Janet, Jon Paul, Duan, Chenru, Getman, Rachel B., and Kulik, Heather J. Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation. United States: N. p., 2019. Web. doi:10.1021/acscatal.9b02165.
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Nandy, Aditya, Zhu, Jiazhou, Janet, Jon Paul, Duan, Chenru, Getman, Rachel B., & Kulik, Heather J. Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation. United States. https://doi.org/10.1021/acscatal.9b02165
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Nandy, Aditya, Zhu, Jiazhou, Janet, Jon Paul, Duan, Chenru, Getman, Rachel B., and Kulik, Heather J. Tue . "Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation". United States. https://doi.org/10.1021/acscatal.9b02165. https://www.osti.gov/servlets/purl/1560618.
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@article{osti_1560618,
title = {Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation},
author = {Nandy, Aditya and Zhu, Jiazhou and Janet, Jon Paul and Duan, Chenru and Getman, Rachel B. and Kulik, Heather J.},
abstractNote = {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.},
doi = {10.1021/acscatal.9b02165},
journal = {ACS Catalysis},
number = 9,
volume = 9,
place = {United States},
year = {2019},
month = {7}
}
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https://doi.org/10.1021/acscatal.9b02165
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Figures / Tables:

Figure 1 Figure 1: Representative structures for the equatorially symmetric (top) and equatorially asymmetric (bottom) data sets, which each have up to two unique ligand types, L1 and L2 (here L1 = CN−, L2 = NH3). The metal is shown as an orange sphere, and other atoms are shown as sticks, withmore » oxygen in red, nitrogen in blue, and carbon in gray.« less
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  • Li, Zheng; Omidvar, Noushin; Chin, Wei Shan
  • The Journal of Physical Chemistry A, Vol. 122, Issue 18
  • DOI: 10.1021/acs.jpca.8b02842

Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design
journal, April 2017

  • Janet, Jon Paul; Gani, Terry Z. H.; Steeves, Adam H.
  • Industrial & Engineering Chemistry Research, Vol. 56, Issue 17
  • DOI: 10.1021/acs.iecr.7b00808

Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships
journal, November 2017

  • Janet, Jon Paul; Kulik, Heather J.
  • The Journal of Physical Chemistry A, Vol. 121, Issue 46
  • DOI: 10.1021/acs.jpca.7b08750

Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network
journal, February 2018

  • Janet, Jon Paul; Chan, Lydia; Kulik, Heather J.
  • The Journal of Physical Chemistry Letters, Vol. 9, Issue 5
  • DOI: 10.1021/acs.jpclett.8b00170

Predicting electronic structure properties of transition metal complexes with neural networks
journal, January 2017

  • Janet, Jon Paul; Kulik, Heather J.
  • Chemical Science, Vol. 8, Issue 7
  • DOI: 10.1039/C7SC01247K

A quantitative uncertainty metric controls error in neural network-driven chemical discovery
journal, January 2019

  • Janet, Jon Paul; Duan, Chenru; Yang, Tzuhsiung
  • Chemical Science, Vol. 10, Issue 34
  • DOI: 10.1039/C9SC02298H

Dynamics of Subnanometer Pt Clusters Can Break the Scaling Relationships in Catalysis
journal, January 2019

  • Zandkarimi, Borna; Alexandrova, Anastassia N.
  • The Journal of Physical Chemistry Letters, Vol. 10, Issue 3
  • DOI: 10.1021/acs.jpclett.8b03680

Lonely Atoms with Special Gifts: Breaking Linear Scaling Relationships in Heterogeneous Catalysis with Single-Atom Alloys
journal, September 2018

  • Darby, Matthew T.; Stamatakis, Michail; Michaelides, Angelos
  • The Journal of Physical Chemistry Letters, Vol. 9, Issue 18
  • DOI: 10.1021/acs.jpclett.8b01888

Quantum Chemistry on Graphical Processing Units. 3. Analytical Energy Gradients, Geometry Optimization, and First Principles Molecular Dynamics
journal, August 2009

  • Ufimtsev, Ivan S.; Martinez, Todd J.
  • Journal of Chemical Theory and Computation, Vol. 5, Issue 10
  • DOI: 10.1021/ct9003004

Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density
journal, January 1988

Density‐functional thermochemistry. III. The role of exact exchange
journal, April 1993

  • Becke, Axel D.
  • The Journal of Chemical Physics, Vol. 98, Issue 7, p. 5648-5652
  • DOI: 10.1063/1.464913

Ab Initio Calculation of Vibrational Absorption and Circular Dichroism Spectra Using Density Functional Force Fields
journal, November 1994

  • Stephens, P. J.; Devlin, F. J.; Chabalowski, C. F.
  • The Journal of Physical Chemistry, Vol. 98, Issue 45, p. 11623-11627
  • DOI: 10.1021/j100096a001

A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu
journal, April 2010

  • Grimme, Stefan; Antony, Jens; Ehrlich, Stephan
  • The Journal of Chemical Physics, Vol. 132, Issue 15
  • DOI: 10.1063/1.3382344

A density-functional model of the dispersion interaction
journal, October 2005

  • Becke, Axel D.; Johnson, Erin R.
  • The Journal of Chemical Physics, Vol. 123, Issue 15
  • DOI: 10.1063/1.2065267

A post-Hartree–Fock model of intermolecular interactions
journal, July 2005

  • Johnson, Erin R.; Becke, Axel D.
  • The Journal of Chemical Physics, Vol. 123, Issue 2
  • DOI: 10.1063/1.1949201

A post-Hartree-Fock model of intermolecular interactions: Inclusion of higher-order corrections
journal, May 2006

  • Johnson, Erin R.; Becke, Axel D.
  • The Journal of Chemical Physics, Vol. 124, Issue 17
  • DOI: 10.1063/1.2190220

Ab initio effective core potentials for molecular calculations. Potentials for the transition metal atoms Sc to Hg
journal, January 1985

  • Hay, P. Jeffrey; Wadt, Willard R.
  • The Journal of Chemical Physics, Vol. 82, Issue 1
  • DOI: 10.1063/1.448799

molSimplify: A toolkit for automating discovery in inorganic chemistry
journal, July 2016

  • Ioannidis, Efthymios I.; Gani, Terry Z. H.; Kulik, Heather J.
  • Journal of Computational Chemistry, Vol. 37, Issue 22
  • DOI: 10.1002/jcc.24437

Mechanism of Oxidation of Ethane to Ethanol at Iron(IV)–Oxo Sites in Magnesium-Diluted Fe 2 (dobdc)
journal, April 2015

  • Verma, Pragya; Vogiatzis, Konstantinos D.; Planas, Nora
  • Journal of the American Chemical Society, Vol. 137, Issue 17
  • DOI: 10.1021/jacs.5b00382

Accurate Coulomb-fitting basis sets for H to Rn
journal, January 2006

  • Weigend, Florian
  • Physical Chemistry Chemical Physics, Vol. 8, Issue 9
  • DOI: 10.1039/b515623h

Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms
conference, January 2013

Water-Soluble Iron(IV)-Oxo Complexes Supported by Pentapyridine Ligands: Axial Ligand Effects on Hydrogen Atom and Oxygen Atom Transfer Reactivity
journal, May 2015

Communication: Avoiding unbound anions in density functional calculations
journal, May 2011

  • Kim, Min-Cheol; Sim, Eunji; Burke, Kieron
  • The Journal of Chemical Physics, Vol. 134, Issue 17
  • DOI: 10.1063/1.3590364

Describing Anions by Density Functional Theory: Fractional Electron Affinity
journal, August 2010

  • Jensen, Frank
  • Journal of Chemical Theory and Computation, Vol. 6, Issue 9
  • DOI: 10.1021/ct1003324

C−H Bond Activations by Metal Oxo Compounds
journal, February 2010

  • Gunay, Ahmet; Theopold, Klaus H.
  • Chemical Reviews, Vol. 110, Issue 2
  • DOI: 10.1021/cr900269x

The Electronic Structure of the Vanadyl Ion
journal, February 1962

  • Ballhausen, C. J.; Gray, Harry B.
  • Inorganic Chemistry, Vol. 1, Issue 1
  • DOI: 10.1021/ic50001a022

Synthesis and reactivity of a mononuclear non-haem cobalt(IV)-oxo complex
journal, March 2017

  • Wang, Bin; Lee, Yong-Min; Tcho, Woon-Young
  • Nature Communications, Vol. 8, Issue 1
  • DOI: 10.1038/ncomms14839

Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models
journal, March 2019

  • Duan, Chenru; Janet, Jon Paul; Liu, Fang
  • Journal of Chemical Theory and Computation, Vol. 15, Issue 4
  • DOI: 10.1021/acs.jctc.9b00057
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    Works referencing / citing this record:

    Reaction mechanisms at the homogeneous–heterogeneous frontier: insights from first-principles studies on ligand-decorated metal nanoparticles
    journal, January 2019

    • Ortuño, Manuel A.; López, Núria
    • Catalysis Science & Technology, Vol. 9, Issue 19
    • DOI: 10.1039/c9cy01351b

    In silico high throughput screening of bimetallic and single atom alloys using machine learning and ab initio microkinetic modelling
    journal, January 2020

    • Saxena, Shivam; Khan, Tuhin Suvra; Jalid, Fatima
    • Journal of Materials Chemistry A, Vol. 8, Issue 1
    • DOI: 10.1039/c9ta07651d
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