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Title: Making machine learning a useful tool in the accelerated discovery of transition metal complexes

Abstract

Abstract As machine learning (ML) has matured, it has opened a new frontier in theoretical and computational chemistry by offering the promise of simultaneous paradigm shifts in accuracy and efficiency. Nowhere is this advance more needed, but also more challenging to achieve, than in the discovery of open‐shell transition metal complexes. Here, localized d or f electrons exhibit variable bonding that is challenging to capture even with the most computationally demanding methods. Thus, despite great promise, clear obstacles remain in constructing ML models that can supplement or even replace explicit electronic structure calculations. In this article, I outline the recent advances in building ML models in transition metal chemistry, including the ability to approach sub‐kcal/mol accuracy on a range of properties with tailored representations, to discover and enumerate complexes in large chemical spaces, and to reveal opportunities for design through analysis of feature importance. I discuss unique considerations that have been essential to enabling ML in open‐shell transition metal chemistry, including (a) the relationship of data set size/diversity, model complexity, and representation choice, (b) the importance of quantitative assessments of both theory and model domain of applicability, and (c) the need to enable autonomous generation of reliable, large data setsmore » both for ML model training and in active learning or discovery contexts. Finally, I summarize the next steps toward making ML a mainstream tool in the accelerated discovery of transition metal complexes. This article is categorized under: Electronic Structure Theory > Density Functional Theory Software > Molecular Modeling Computer and Information Science > Chemoinformatics« less

Authors:
ORCiD logo [1]
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  1. Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1546100
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Wiley Interdisciplinary Reviews: Computational Molecular Science
Additional Journal Information:
Journal Name: Wiley Interdisciplinary Reviews: Computational Molecular Science Journal Volume: 10 Journal Issue: 1; Journal ID: ISSN 1759-0876
Publisher:
Wiley Blackwell (John Wiley & Sons)
Country of Publication:
United States
Language:
English

Citation Formats

Kulik, Heather J. Making machine learning a useful tool in the accelerated discovery of transition metal complexes. United States: N. p., 2019. Web. doi:10.1002/wcms.1439.
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Kulik, Heather J. Making machine learning a useful tool in the accelerated discovery of transition metal complexes. United States. https://doi.org/10.1002/wcms.1439
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Kulik, Heather J. Fri . "Making machine learning a useful tool in the accelerated discovery of transition metal complexes". United States. https://doi.org/10.1002/wcms.1439.
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@article{osti_1546100,
title = {Making machine learning a useful tool in the accelerated discovery of transition metal complexes},
author = {Kulik, Heather J.},
abstractNote = {Abstract As machine learning (ML) has matured, it has opened a new frontier in theoretical and computational chemistry by offering the promise of simultaneous paradigm shifts in accuracy and efficiency. Nowhere is this advance more needed, but also more challenging to achieve, than in the discovery of open‐shell transition metal complexes. Here, localized d or f electrons exhibit variable bonding that is challenging to capture even with the most computationally demanding methods. Thus, despite great promise, clear obstacles remain in constructing ML models that can supplement or even replace explicit electronic structure calculations. In this article, I outline the recent advances in building ML models in transition metal chemistry, including the ability to approach sub‐kcal/mol accuracy on a range of properties with tailored representations, to discover and enumerate complexes in large chemical spaces, and to reveal opportunities for design through analysis of feature importance. I discuss unique considerations that have been essential to enabling ML in open‐shell transition metal chemistry, including (a) the relationship of data set size/diversity, model complexity, and representation choice, (b) the importance of quantitative assessments of both theory and model domain of applicability, and (c) the need to enable autonomous generation of reliable, large data sets both for ML model training and in active learning or discovery contexts. Finally, I summarize the next steps toward making ML a mainstream tool in the accelerated discovery of transition metal complexes. This article is categorized under: Electronic Structure Theory > Density Functional Theory Software > Molecular Modeling Computer and Information Science > Chemoinformatics},
doi = {10.1002/wcms.1439},
journal = {Wiley Interdisciplinary Reviews: Computational Molecular Science},
number = 1,
volume = 10,
place = {United States},
year = {2019},
month = {8}
}
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Towards operando computational modeling in heterogeneous catalysis
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  • Grajciar, Lukáš; Heard, Christopher J.; Bondarenko, Anton A.
  • Chemical Society Reviews, Vol. 47, Issue 22
  • DOI: 10.1039/C8CS00398J

Computational Ligand Descriptors for Catalyst Design
journal, October 2018

Comparing molecules and solids across structural and alchemical space
journal, January 2016

  • De, Sandip; Bartók, Albert P.; Csányi, Gábor
  • Physical Chemistry Chemical Physics, Vol. 18, Issue 20
  • DOI: 10.1039/C6CP00415F

The AFLOW standard for high-throughput materials science calculations
journal, October 2015

Feed-forward neural networks in chemistry: mathematical systems for classification and pattern recognition
journal, December 1993

  • Burns, John A.; Whitesides, George M.
  • Chemical Reviews, Vol. 93, Issue 8
  • DOI: 10.1021/cr00024a001

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

Accelerating materials property predictions using machine learning
journal, September 2013

  • Pilania, Ghanshyam; Wang, Chenchen; Jiang, Xun
  • Scientific Reports, Vol. 3, Issue 1
  • DOI: 10.1038/srep02810

Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids
journal, January 1996

  • Jorgensen, William L.; Maxwell, David S.; Tirado-Rives, Julian
  • Journal of the American Chemical Society, Vol. 118, Issue 45
  • DOI: 10.1021/ja9621760

Open Babel: An open chemical toolbox
journal, October 2011

  • O'Boyle, Noel M.; Banck, Michael; James, Craig A.
  • Journal of Cheminformatics, Vol. 3, Issue 1
  • DOI: 10.1186/1758-2946-3-33

Spin-state diversity in a series of Co( ii ) PNP pincer bromide complexes
journal, January 2016

  • Shaffer, David W.; Bhowmick, Indrani; Rheingold, Arnold L.
  • Dalton Transactions, Vol. 45, Issue 44
  • DOI: 10.1039/C6DT03461F

Embedded Correlated Wavefunction Schemes: Theory and Applications
journal, May 2014

  • Libisch, Florian; Huang, Chen; Carter, Emily A.
  • Accounts of Chemical Research, Vol. 47, Issue 9
  • DOI: 10.1021/ar500086h

The performance of nonhybrid density functionals for calculating the structures and spin states of Fe(II) and Fe(III) complexes
journal, November 2004

  • Deeth, Robert J.; Fey, Natalie
  • Journal of Computational Chemistry, Vol. 25, Issue 15
  • DOI: 10.1002/jcc.20101

High-Dimensional Neural Network Potentials for Organic Reactions and an Improved Training Algorithm
journal, April 2015

  • Gastegger, Michael; Marquetand, Philipp
  • Journal of Chemical Theory and Computation, Vol. 11, Issue 5
  • DOI: 10.1021/acs.jctc.5b00211

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

Internal force corrections with machine learning for quantum mechanics/molecular mechanics simulations
journal, October 2017

  • Wu, Jingheng; Shen, Lin; Yang, Weitao
  • The Journal of Chemical Physics, Vol. 147, Issue 16
  • DOI: 10.1063/1.5006882

Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry
journal, September 2018

  • Nandy, Aditya; Duan, Chenru; Janet, Jon Paul
  • Industrial & Engineering Chemistry Research, Vol. 57, Issue 42
  • DOI: 10.1021/acs.iecr.8b04015

Reduction of Systematic Uncertainty in DFT Redox Potentials of Transition-Metal Complexes
journal, March 2012

  • Konezny, Steven J.; Doherty, Mark D.; Luca, Oana R.
  • The Journal of Physical Chemistry C, Vol. 116, Issue 10
  • DOI: 10.1021/jp300485t

Tuning the Electronic Structure of Fe(II) Polypyridines via Donor Atom and Ligand Scaffold Modifications: A Computational Study
journal, August 2015

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
journal, July 2013

  • Hansen, Katja; Montavon, Grégoire; Biegler, Franziska
  • Journal of Chemical Theory and Computation, Vol. 9, Issue 8
  • DOI: 10.1021/ct400195d

Density functional theory for modelling large molecular adsorbate–surface interactions: a mini-review and worked example
journal, November 2016

AFLOW: An automatic framework for high-throughput materials discovery
journal, June 2012

Multiconfiguration Pair-Density Functional Theory for Iron Porphyrin with CAS, RAS, and DMRG Active Spaces
journal, February 2019

  • Zhou, Chen; Gagliardi, Laura; Truhlar, Donald G.
  • The Journal of Physical Chemistry A, Vol. 123, Issue 15
  • DOI: 10.1021/acs.jpca.8b12479

Optimization of parameters for semiempirical methods V: Modification of NDDO approximations and application to 70 elements
journal, September 2007

Application of Semiempirical Methods to Transition Metal Complexes: Fast Results but Hard-to-Predict Accuracy
journal, May 2018

  • Minenkov, Yury; Sharapa, Dmitry I.; Cavallo, Luigi
  • Journal of Chemical Theory and Computation, Vol. 14, Issue 7
  • DOI: 10.1021/acs.jctc.8b00018

SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
journal, February 1988

  • Weininger, David
  • Journal of Chemical Information and Modeling, Vol. 28, Issue 1
  • DOI: 10.1021/ci00057a005

The promise of artificial intelligence in chemical engineering: Is it here, finally?
journal, December 2018

Ruthenium(II)-Catalyzed C–H Bond Activation and Functionalization
journal, August 2012

  • Arockiam, Percia Beatrice; Bruneau, Christian; Dixneuf, Pierre H.
  • Chemical Reviews, Vol. 112, Issue 11
  • DOI: 10.1021/cr300153j

Computational Approach to Molecular Catalysis by 3d Transition Metals: Challenges and Opportunities
journal, October 2018

  • Vogiatzis, Konstantinos D.; Polynski, Mikhail V.; Kirkland, Justin K.
  • Chemical Reviews, Vol. 119, Issue 4
  • DOI: 10.1021/acs.chemrev.8b00361

Quantum Chemistry on Graphical Processing Units. 2. Direct Self-Consistent-Field Implementation
journal, March 2009

  • Ufimtsev, Ivan S.; Martinez, Todd J.
  • Journal of Chemical Theory and Computation, Vol. 5, Issue 4
  • DOI: 10.1021/ct800526s

A Shape Index from Molecular Graphs
journal, January 1985

Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity
journal, October 2016

  • Huang, Bing; von Lilienfeld, O. Anatole
  • The Journal of Chemical Physics, Vol. 145, Issue 16
  • DOI: 10.1063/1.4964627

Spin State Energetics in First-Row Transition Metal Complexes: Contribution of (3s3p) Correlation and Its Description by Second-Order Perturbation Theory
journal, January 2017

  • Pierloot, Kristine; Phung, Quan Manh; Domingo, Alex
  • Journal of Chemical Theory and Computation, Vol. 13, Issue 2
  • DOI: 10.1021/acs.jctc.6b01005

Automated Selection of Active Orbital Spaces
journal, March 2016

  • Stein, Christopher J.; Reiher, Markus
  • Journal of Chemical Theory and Computation, Vol. 12, Issue 4
  • DOI: 10.1021/acs.jctc.6b00156

Combining Linear-Scaling DFT with Subsystem DFT in Born–Oppenheimer and Ehrenfest Molecular Dynamics Simulations: From Molecules to a Virus in Solution
journal, June 2016

  • Andermatt, Samuel; Cha, Jinwoong; Schiffmann, Florian
  • Journal of Chemical Theory and Computation, Vol. 12, Issue 7
  • DOI: 10.1021/acs.jctc.6b00398

Local treatment of electron correlation in coupled cluster theory
journal, April 1996

  • Hampel, Claudia; Werner, Hans‐Joachim
  • The Journal of Chemical Physics, Vol. 104, Issue 16
  • DOI: 10.1063/1.471289

A general-purpose machine learning framework for predicting properties of inorganic materials
journal, August 2016

Quantum chemistry structures and properties of 134 kilo molecules
journal, August 2014

  • Ramakrishnan, Raghunathan; Dral, Pavlo O.; Rupp, Matthias
  • Scientific Data, Vol. 1, Issue 1
  • DOI: 10.1038/sdata.2014.22

Bridging the Homogeneous-Heterogeneous Divide: Modeling Spin for Reactivity in Single Atom Catalysis
journal, April 2019

Random Forests
journal, January 2001

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