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];
[1]
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Publication Date:
- Research Org.:
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- 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 Name: Journal of Physical Chemistry Letters 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.
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. https://doi.org/10.1021/acs.jpclett.8b00170
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. https://doi.org/10.1021/acs.jpclett.8b00170.
@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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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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