Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network
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acs.jpclett.8b00170.pdf
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2.5 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Janet, Jon Paul
Chan, Lydia C.
Kulik, Heather Janine
Date Issued
February 2018
Journal
Journal of Physical Chemistry Letters
Publisher
American Chemical Society (ACS)
Citation
Janet, Jon Paul et al. “Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network.” The Journal of Physical Chemistry Letters 9, 5 (February 2018): 1064–1071 © 2018 American Chemical Society
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1021/ACS.JPCLETT.8B00170
