Accelerating Chemical Discovery with Machine Learning:
Simulated Evolution of Spin Crossover Complexes with an Artificial
Neural Network
Version 3 2018-03-02, 15:37
Version 2 2018-02-21, 00:43
Version 1 2018-02-15, 20:51
Posted on 2018-03-02 - 15:37
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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Janet, Jon Paul; Chan, Lydia; Kulik, Heather J. (2018). Accelerating Chemical Discovery with Machine Learning:
Simulated Evolution of Spin Crossover Complexes with an Artificial
Neural Network. ACS Publications. Collection. https://doi.org/10.1021/acs.jpclett.8b00170
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AUTHORS (3)
JJ
Jon Paul Janet
LC
Lydia Chan
HK
Heather J. Kulik
KEYWORDS
material discoveryArtificial Neural Network Machinematerials discoverycombinationerror-aware ML-driven discoveryspin-state splittingchemical space enumerationcompound spaceSimulated EvolutionAccelerating Chemical Discovery32- ligand poolkcalstrategy5600 candidate materialsfirst-principles methodsSpin Crossover Complexesspin-crossover complexesDFT-driven GAANN training pointsMachine Learning