Discovery of Spin-Crossover Candidates with Equivariant Graph Neural Networks and Relevance-Based Classification
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912225236615168 |
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| author | Albavera-Mata, Angel Prakash, Pawan Gibson, Jason B. Fonseca, Eric Ren, Sijin Zhang, Xiao-Guang Cheng, Hai-Ping Shatruk, Michael Trickey, S. B. Hennig, Richard G. |
| author_facet | Albavera-Mata, Angel Prakash, Pawan Gibson, Jason B. Fonseca, Eric Ren, Sijin Zhang, Xiao-Guang Cheng, Hai-Ping Shatruk, Michael Trickey, S. B. Hennig, Richard G. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05341 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Discovery of Spin-Crossover Candidates with Equivariant Graph Neural Networks and Relevance-Based Classification Albavera-Mata, Angel Prakash, Pawan Gibson, Jason B. Fonseca, Eric Ren, Sijin Zhang, Xiao-Guang Cheng, Hai-Ping Shatruk, Michael Trickey, S. B. Hennig, Richard G. Disordered Systems and Neural Networks Materials Science 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. |
| title | Discovery of Spin-Crossover Candidates with Equivariant Graph Neural Networks and Relevance-Based Classification |
| topic | Disordered Systems and Neural Networks Materials Science |
| url | https://arxiv.org/abs/2501.05341 |