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RESEARCH PAPER · ChemRxiv · 2020

The tmQM Dataset - Quantum Geometries and Properties of 86k Transition Metal Complexes

David Balcells, Bastian Bjerkem Skjelstad

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The tmQM Dataset - Quantum Geometries and Properties of 86k Transition Metal ComplexesOriginalBúsqueda de complejos de Fe con potencial actividad vía el Efecto caballo de Troya en Escherichia coli1TMtopo Dataset — Quantum Geometries and Density Topology for 1.1k Transition Metal Complexes0TMtopo Dataset — Quantum Geometries and Density Topology for 1.1k Transition Metal Complexes0Evolutionary Multiobjective Optimization of Multiligand Metal Complexes in Diverse and Vast Chemical Spaces1Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_rev Dataset2ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design2AABBA: Atom–Atom Bond–Bond Bond–Atom Graph Kernel for Machine Learning on Molecules and Materials0Deep Graph Learning for Molecules and Materials3Automated Prediction of Ground State Spin for Transition Metal Complexes0Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes1Deep Generative Model for the Dual-Objective Inverse Design of Metal Complexes2

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  1. Búsqueda de complejos de Fe con potencial actividad vía el Efecto caballo de Troya en Escherichia coli

    Revista ECIPeru · 2021-01-01

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  2. TMtopo Dataset — Quantum Geometries and Density Topology for 1.1k Transition Metal Complexes

    ChemRxiv · 2021-02-24

    Filipe Teixeira, Edgar Silva-Santos, M. Natália D. S. Cordeiro

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  3. TMtopo Dataset — Quantum Geometries and Density Topology for 1.1k Transition Metal Complexes

    ChemRxiv · 2021-02-25

    Filipe B. Teixeira, Edgar Silva-Santos, M. Natália D. S. Cordeiro

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  4. Evolutionary Multiobjective Optimization of Multiligand Metal Complexes in Diverse and Vast Chemical Spaces

    ChemRxiv · 2023-06-21

    Hannes Kneiding, Ainara Nova, David Balcells

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  5. Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_rev Dataset

    ChemRxiv · 2023-08-04

    Aaron G. Garrison, Javier Heras‐Domingo, John R. Kitchin, Gabe Gomes, Zachary W. Ulissi, Samuel M. Blau

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  6. ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design

    ChemRxiv · 2023-08-17

    Daniel H. Ess, Shusen Chen, Zack Meyer, Brendan Jensen, Alex Kraus, Allison Lambert

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  7. AABBA: Atom–Atom Bond–Bond Bond–Atom Graph Kernel for Machine Learning on Molecules and Materials

    ChemRxiv · 2023-09-05

    Lucía Morán‐González, Jørn Eirik Betten, Hannes Kneiding, David Balcells

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  8. Deep Graph Learning for Molecules and Materials

    Nordic Machine Intelligence · 2023-12-21

    Hannes Kneiding, David Balcells

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  9. Automated Prediction of Ground State Spin for Transition Metal Complexes

    ChemRxiv · 2024-03-24

    Yuri Cho, Rubén Laplaza, Sergi Vela, Clémence Corminbœuf

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  10. Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes

    ChemRxiv · 2024-05-05

    Ilia Kevlishvili, Roland G. St. Michel, Aaron G. Garrison, Jacob Toney, Husain Adamji, Haojun Jia, Heather J. Kulik

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  11. Deep Generative Model for the Dual-Objective Inverse Design of Metal Complexes

    ChemRxiv · 2024-05-29

    Magnus Strandgaard, Trond Linjordet, Hannes Kneiding, Arron L. Burnage, Ainara Nova, Jan Halborg Jensen, David Balcells

    Read original paper ↗ ·
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