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

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.