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 comp...
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| Format: | Article |
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American Chemical Society (ACS)
2019
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| Online Access: | http://hdl.handle.net/1721.1/120162 https://orcid.org/0000-0001-7825-4797 https://orcid.org/0000-0001-9342-0191 |