ArticleJournal of chemical information and modeling2026
Machine Learning Prediction for Fe(II) Spin-Crossover Complex in the Same Spin State Using Geometrical and Topological Descriptors.
Article in Journal of chemical information and modeling, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Spin-crossover (SCO) complexes are molecular materials capable of reversibly switching between high-spin (HS) and low-spin (LS) states in response to external stimuli. Predicting SCO from crystallographic data allows for the efficient design of new SCO complexes. However, no extensive and diverse data sets of complexes with annotated SCO characteristics were available. In the present study, we manually assembled a data set of 500 Fe(II)-N
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