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

doi:10.1021/acs.jpclett.8b00170

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

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.

Authors:

^[1]; Chan, Lydia ^[1];

^[1]

Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Chemical Engineering

Publication Date:: 2018-02-15

Research Org.:: Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)

OSTI Identifier:: 1421993

Alternate Identifier(s):: OSTI ID: 1508752

Grant/Contract Number:: SC0018096; N00014-17-1-2956; CBET-1704266; ACI-1548562

Resource Type:: Published Article

Journal Name:: Journal of Physical Chemistry Letters

Additional Journal Information:: Journal Volume: 9; Journal Issue: 5; Journal ID: ISSN 1948-7185

Publisher:: American Chemical Society

Country of Publication:: United States

Language:: English

Subject:: 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; 36 MATERIALS SCIENCE

Citation Formats


                    Janet, Jon Paul, Chan, Lydia, and Kulik, Heather J. Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network.  United States: N. p., 2018. 
        Web.  doi:10.1021/acs.jpclett.8b00170.

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                    Janet, Jon Paul, Chan, Lydia, & Kulik, Heather J. Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network.  United States.  doi:10.1021/acs.jpclett.8b00170.

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                    Janet, Jon Paul, Chan, Lydia, and Kulik, Heather J. Thu .  
        "Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network".  United States.  doi:10.1021/acs.jpclett.8b00170.

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@article{osti_1421993,

  title        = {Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network},

  author       = {Janet, Jon Paul and Chan, Lydia and Kulik, Heather J.},

  abstractNote = {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.},

  doi          = {10.1021/acs.jpclett.8b00170},

  journal      = {Journal of Physical Chemistry Letters},

  number       = 5,

  volume       = 9,

  place        = {United States},

  year         = {2018},

  month        = {2}

}

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DOI: 10.1021/acs.jpclett.8b00170

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Cited by: 20 works

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Figures / Tables:

Figure 1.: (top) Representative descriptors in MCDL-25: metal properties, metal-adjacent (i.e., local ligand properties), and global ligand properties. (bottom) Representative complexes including Fe(II)(H₂O)₆ in training data and increasingly distant complexes from the training data (left to right): Fe(II)(bpy)₃, Fe(II)(H₂O)₂(furan)₄, and Fe(II)(bpy)₂(furan)₂. The closest training point and its distance is indicatedmore »

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