Discovery of Spin-Crossover Candidates with Equivariant Graph Neural Networks and Relevance-Based Classification

Angel Albavera-Mata; Pawan Prakash; Jason B. Gibson; Eric Fonseca; Sijin Ren; Xiao-Guang Zhang; Hai-Ping Cheng; Michael Shatruk; S. B. Trickey; Richard G. Hennig

arXiv · 2501.05341

Discovery of Spin-Crossover Candidates with Equivariant Graph Neural Networks and Relevance-Based Classification

Angel Albavera-Mata·Pawan Prakash·Jason B. Gibson·Eric Fonseca·Sijin Ren·Xiao-Guang Zhang·Hai-Ping Cheng·Michael Shatruk·S. B. Trickey·Richard G. Hennig

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Abstract

Swift discovery of spin-crossover materials for their potential application in quantum information devices requires techniques which enable efficient identification of suitably bistable candidates. To this end, we screened the Cambridge Structural Database to develop a specialized database of 1,439 materials and computed spin-switching energies from density functional theory for each material. The database was used to train an equivariant graph convolutional neural network to predict the magnitude of the spin-conversion energy. A test mean absolute error was 360 meV. For candidate identification, we equipped the system with a relevance-based classifier. This approach leads to a nearly four-fold improvement in identifying potential spin-crossover systems of interest as compared to conventional high-throughput screening.

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Angel Albavera-Mata, Pawan Prakash, Jason B. Gibson, Eric Fonseca, Sijin Ren, Xiao-Guang Zhang, Hai-Ping Cheng, Michael Shatruk, S. B. Trickey, Richard G. Hennig. 2025-01-09. Discovery of Spin-Crossover Candidates with Equivariant Graph Neural Networks and Relevance-Based Classification. https://arxiv.org/abs/2501.05341

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