ArticleJournal of chemical information and modeling2020
tmQM Dataset-Quantum Geometries and Properties of 86k Transition Metal Complexes.
Article in Journal of chemical information and modeling, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed.
- POMs-GibbsNet: A Deep Neural Network for the Prediction of Thermodynamic Parameters of Polyoxometalates.Journal of computational chemistry · 2026Article
- AI-EXAFS: Physics-Informed Prediction of EXAFS from Atomic Structure.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- 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
- Computational data as the fuel for AI in chemistry.Chemical science · 2026Review
- tmQM-RDF Data Set: A Knowledge Graph Representing Transition Metal Complexes.Journal of chemical information and modeling · 2026Article
- The Future of Foundation Machine Learning Potentials and DFT in Homogeneous Catalysis: Competition or Synergy?Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Article
- A 50,688-Reaction Data Set Reveals General Ligands and Mechanistic Diversity in C-N Couplings.Journal of the American Chemical Society · 2026Article
- 3DOpt: Benchmark for Automated Design of 3D Molecular Structures across the Periodic Table.Journal of chemical information and modeling · 2026Article
- From Local Atomic Structure to X-ray Spectra: Absorber-Centric Machine-Learning Encoding.The journal of physical chemistry. A · 2026Article
- Benchmarking physics-inspired machine learning models for transition metal complexes with diverse charge and spin states.Digital discovery · 2026Article
- When does global attention help: a unified empirical study on atomistic graph learning.Journal of cheminformatics · 2026Article
- A Comprehensive Dataset of Chemical Reactions Covering Second and Third Row Elements with Million-Scale Quantum Chemical Calculations.Scientific data · 2026Article
- Efficient Machine Learning Prediction of Solvent-DependentJournal of computational chemistry · 2026Article
- Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence.Journal of the American Chemical Society · 2026Review
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- Graph-Based Internal Coordinate Analysis for Transition State Characterization.Journal of chemical theory and computation · 2026Article
- Foundation models for atomistic simulation of chemistry and materials.Nature reviews. Chemistry · 2026Review
- Multi-Solvent Graph Neural Network for Reduction Potential Prediction Across the Chemical Space.Journal of chemical information and modeling · 2026Article
- Representative Random Sampling of Chemical Space.Journal of chemical theory and computation · 2025Article
- Cartesian equivariant representations for learning and understanding molecular orbitals.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
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2 authors.
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Abstract
We report the transition metal quantum mechanics (tmQM) data set, which contains the geometries and properties of a large transition metal-organic compound space. tmQM comprises 86,665 mononuclear complexes extracted from the Cambridge Structural Database, including Werner, bioinorganic, and organometallic complexes based on a large variety of organic ligands and 30 transition metals (the 3d, 4d, and 5d from groups 3 to 12). All complexes are closed-shell, with a formal charge in the range {+1, 0, -1}
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