ArticleThe journal of physical chemistry. A2020
Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions.
Article in The journal of physical chemistry. A, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- BOS-TMC Data Set: DFT Properties of 159k Experimentally Characterized Transition Metal Complexes Spanning Multiple Charge and Spin States.Journal of chemical information and modeling · 2026Article
- Machine Learning Prediction for Fe(II) Spin-Crossover Complex in the Same Spin State Using Geometrical and Topological Descriptors.Journal of chemical information and modeling · 2026Article
- Extensions to Extended Tight-Binding Methods for Transition-Metal Containing Systems.Journal of computational chemistry · 2026Article
- High-Throughput Discovery of Ferrocene Mechanophores with Enhanced Reactivity and Network Toughening.ACS central science · 2025Article
- Automated prediction of ground state spin for transition metal complexes.Digital discovery · 2024Article
- Modeling Fe(II) Complexes Using Neural Networks.Journal of chemical theory and computation · 2024Article
- Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis.Beilstein journal of organic chemistry · 2024Review
- Architector for high-throughput cross-periodic table 3D complex building.Nature communications · 2023Article
- Comparison of the Performance of Density Functional Methods for the Description of Spin States and Binding Energies of Porphyrins.Molecules (Basel, Switzerland) · 2023Article
- Low-cost machine learning prediction of excited state properties of iridium-centered phosphors.Chemical science · 2023Article
- MOFSimplify, machine learning models with extracted stability data of three thousand metal-organic frameworks.Scientific data · 2022Article
- Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles.Chemical science · 2021Article
- Machine Learning for Electronically Excited States of Molecules.Chemical reviews · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Determination of ground-state spins of open-shell transition-metal complexes is critical to understanding catalytic and materials properties but also challenging with approximate electronic structure methods. As an alternative approach, we demonstrate how structure alone can be used to guide assignment of ground-state spin from experimentally determined crystal structures of transition-metal complexes. We first identify the limits of distance-based heuristics from distributions of metal-ligand bond lengths of over 2000 unique mononuclear Fe(II)/Fe(III) transition-metal complexes. To overcome these limits, we employ artificial neural networks (ANNs) to predict spin-state-dependent metal-ligand bond lengths and classify experimental ground-state spins based on agreement of experimental structures with the ANN predictions. Although the ANN is trained on hybrid density functional theory data, we exploit the method-insensitivity of geometric properties to enable assignment of ground states for the majority (ca. 80-90%) of structures. We demonstrate the utility of the ANN by data-mining the literature for spin-crossover (SCO) complexes, which have experimentally observed temperature-dependent geometric structure changes, by correctly assigning almost all (>95%) spin states in the 46 Fe(II) SCO complex set. This approach represents a promising complement to more conventional energy-based spin-state assignment from electronic structure theory at the low cost of a machine learning model.
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Registered trials
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