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

Janet, Jon Paul; Chan, Lydia; Kulik, Heather J.

doi:10.1021/acs.jpclett.8b00170

Cited by 200Machine Learning in Materials ScienceMagnetism in coordination complexesAdvanced Chemical Physics Studies

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

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Janet, Jon Paul, Chan, Lydia, Kulik, Heather J.·2018·The Journal of Physical Chemistry Letters

DOI 10.1021/acs.jpclett.8b00170

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Abstract

Machine learning (ML) has emerged as a powerful complement to simulation for materials discovery by reducing time for evaluation of energies and properties at accuracy competitive with first-principles methods. We use genetic algorithm (GA) optimization to discover unconventional spin-crossover complexes in combination with efficient scoring from an artificial neural network (ANN) that predicts spin-state splitting of inorganic complexes. We explore a compound space of over 5600 candidate materials derived from eight metal/oxidation state combinations and a 32-ligand pool. We introduce a strategy for error-aware ML-driven discovery by limiting how far the GA travels away from the nearest ANN training points while maximizing property (i.e., spin-splitting) fitness, leading to discovery of 80% of the leads from full chemical space enumeration. Over a 51-complex subset, average unsigned errors (4.5 kcal/mol) are close to the ANN’s baseline 3 kcal/mol error. By obtaining leads from the trained ANN within seconds rather than days from a DFT-driven GA, this strategy demonstrates the power of ML for accelerating inorganic material discovery.

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Optimization of a single property is feasible with either direct experimentation or physics-based modeling but can often be accelerated with machine learning.
“Optimization of a single property is feasible with either direct experimentation (117, 118) or physics-based modeling (119) but can often be accelerated with ML (43, 120, 121 ).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [120]
Because fitness evaluation dictates the overall time for optimization, multiple linear regression or quantitative structure-property relationship models, force field or semiempirical models, or machine learning regression models are preferable to full density functional theory evaluation.
“Because fitness evaluation dictates the overall time for optimization, MLR (i.e., quantitative structure-property relationship, or QSPR) models (59,60), force field or semiempirical models (36), or ML regression models (e.g., with ANNs) (120) are preferable in comparison to full DFT evaluation.”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [120]

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Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning
Lu, S., Zhou, Q., Ouyang, Y., Guo, Y., Li, Q., & Wang, J. (2018) · Nature Communications · doi:10.1038/s41467-018-05761-w · Cited by 725 · see citing works
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Bai, B., Guo, Z., Zhou, C., Zhang, W., & Zhang, J. (2020) · Information Sciences · doi:10.1016/j.ins.2020.07.069 · Cited by 100 · see citing works
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High-dimensional neural network potentials for magnetic systems using spin-dependent atom-centered symmetry functions
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Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation
Nandy, A., Zhu, J., Janet, J. P., Duan, C., Getman, R. B., & Kulik, H. J. (2019) · ACS Catalysis · doi:10.1021/acscatal.9b02165 · Cited by 91 · see citing works
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Gomes, G., Pollice, R., & Aspuru‐Guzik, A. (2021) · Trends in Chemistry · doi:10.1016/j.trechm.2020.12.006 · Cited by 87 · see citing works
Computational design of magnetic molecules and their environment using quantum chemistry, machine learning and multiscale simulations
Lunghi, A., & Sanvito, S. (2022) · Nature Reviews Chemistry · doi:10.1038/s41570-022-00424-3 · Cited by 90 · see citing works
Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models
Duan, C., Janet, J. P., Liu, F., Nandy, A., & Kulik, H. J. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00057 · Cited by 87 · see citing works
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Chan, S. Y., & Chau, C. K. (2019) · Building and Environment · doi:10.1016/j.buildenv.2019.106364 · Cited by 85 · see citing works
Error-Controlled Exploration of Chemical Reaction Networks with Gaussian Processes
Simm, G. N. C., & Reiher, M. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00504 · Cited by 78 · see citing works
Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulations
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High-Throughput Screening Approach for the Optoelectronic Properties of Conjugated Polymers
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Machine Learning in Chemistry
Janet, J. P., & Kulik, H. J. (2020) · ACS in focus · doi:10.1021/acs.infocus.7e4001 · Cited by 77 · see citing works
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Saxena, S., Khan, T. S., Jalid, F., Ramteke, M., & Haider, M. A. (2019) · Journal of Materials Chemistry A · doi:10.1039/c9ta07651d · Cited by 75 · see citing works
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Taylor, M. G., Yang, T., Lin, S., Nandy, A., Janet, J. P., Duan, C., & Kulik, H. J. (2020) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.0c01458 · Cited by 81 · see citing works
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Machine Learning for the Discovery, Design, and Engineering of Materials.
Duan, C., Nandy, A., & Kulik, H. J. (2022) · Annual Review of Chemical and Biomolecular Engineering · doi:10.1146/annurev-chembioeng-092320-120230 · Cited by 69 · see citing works
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Zhang, J., Hu, P., & Wang, H. (2020) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.0c00406 · Cited by 66 · see citing works
Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention
Kim, H., Na, J., & Lee, W. B. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01289 · Cited by 65 · see citing works
Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening
Liu, F., Duan, C., & Kulik, H. J. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c02288 · Cited by 58 · see citing works
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Machine-Learning Energy Gaps of Porphyrins with Molecular Graph Representations
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Li, S., Zhang, Y., Hu, Y., Wang, B., Sun, S., Yang, X., & He, H. (2021) · Journal of Materiomics · doi:10.1016/j.jmat.2021.02.005 · Cited by 52 · see citing works
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Kulik, H. J. (2019) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1439 · Cited by 47 · see citing works
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Deep Mining Stable and Nontoxic Hybrid Organic–Inorganic Perovskites for Photovoltaics via Progressive Machine Learning
Wu, T., & Wang, J. (2020) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.0c10371 · Cited by 46 · see citing works
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Machine Learning–Driven Models to Predict Prognostic Outcomes in Patients Hospitalized With Heart Failure Using Electronic Health Records: Retrospective Study
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Spin crossover in FeII cyanometallic frameworks
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Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost
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Authors: Janet, J. P., Chan, L., & Kulik, H. J.
Affiliations: Massachusetts Institute of Technology (US)
Corresponding author: Kulik, Heather J.
ORCID: Janet, Jon Paul
Kulik, Heather J.
Venue: The Journal of Physical Chemistry Letters
Publisher: American Chemical Society (ACS)
Published: 2018-02-09
Volume: 9
Issue: 5
Pages: 1064-1071
ISSN: 1948-7185 (ISSN-L)
DOI: 10.1021/acs.jpclett.8b00170
OpenAlex: W2786308452
Cited by: 200
References: 104
Citations by year: 2026: 10 · 2025: 20 · 2024: 16 · 2023: 25 · 2022: 20 · 2021: 30 · 2020: 32 · 2019: 31 · 2018: 16
FWCI: 10.18
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: other-oa
pubs.acs.org/page/policy/authorchoice_termsofuse.html (vor, 11 d)
Locations: The Journal of Physical Chemistry Letters (publishedVersion, other-oa)
PubMed (publishedVersion)
DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
Applied Categorical Structures
Funders: Division of Chemical, Bioengineering, Environmental, and Transport Systems (CBET-1704266)
Burroughs Wellcome Fund (CASI 1010755)
Basic Energy Sciences (DE-SC0018096)
Massachusetts Institute of Technology (NA)
Office of Naval Research Global (N00014-17-1-2956)
Topics: Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Physical Sciences › Materials Science › Electronic, Optical and Magnetic Materials › Magnetism in coordination complexes
Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
Keywords: spin-crossover complexes, artificial neural networks, genetic algorithm, artificial evolution, chemical space exploration, ligand design

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

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