Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex

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Items 101–179 of 179

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2. Screening Diels–Alder reaction space to identify candidate reactions for self-healing polymer applications
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3. Deciphering Cryptic Behavior in Bimetallic Transition-Metal Complexes with Machine Learning
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4. What's Left for a Computational Chemist To Do in the Age of Machine Learning?
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5. CaTS: Toward Scalable and Efficient Transition State Screening for Catalyst Discovery
   Jun Yin, Wentao Li, Honghao Chen, Ju Qiu, Huasheng Feng, Xiangya Xu, Qiuyan Jin, Xiaonan Wang · 2025 · ACS Catalysis
6. molSimplify 2.0: Improved Structure Generation for Automating Discovery in Inorganic Molecular and Reticular Chemistry
   Gianmarco Terrones, Roland G. St. Michel, Jacob Toney, Akash Ball, Yiran Wang, Aaron G. Garrison, Aditya Nandy, Ralf Meyer, Freya Edholm, Changhwan Oh et al. · 2026 · Journal of Chemical Information and Modeling
7. Molecular Design Learned from the Natural Product Porphyra-334: Molecular Generation via Chemical Variational Autoencoder versus Database Mining via Similarity Search, A Comparative Study
   Harada Yuki, Makoto Hatakeyama, Shuichi Maeda, Qi Gao, Kenichi Koizumi, Yuki Sakamoto, Yuuki Ono, Shinichiro Nakamura · 2022 · ACS Omega
8. Benchmarking machine-readable vectors of chemical reactions on computed activation barriers
   Puck van Gerwen, Ksenia R. Briling, Yannick Calvino Alonso, Malte Franke, Clémence Corminbœuf · 2024 · Digital Discovery
9. Boosting the generality of catalytic systems by the synergetic ligand effect in Pd-catalyzed C-N cross-coupling
   Nikolay O. Grebennikov, Daniil A. Boiko, Darya Olegovna Prima, Malena Madiyeva, Mikhail Evgenievich Minyaev, Valentine P. Ananikov · 2023 · Journal of Catalysis
10. Bi-directional prediction of hydrothermal carbonization characteristics of agroforestry and livestock wastes with variable components: Graph learning model-aided waste recycle
    Zi-Kai Lin, Chao-Sheng Li, Rong-Rong Cai, Yan-Qiu Long · 2024 · Journal of Cleaner Production
11. Understanding the Binding Properties of N‐heterocyclic Carbenes through BDE Matrix App
    Lucía Morán‐González, Jaime Rodrı́guez-Guerra, María Besora, Feliu Maseras · 2021 · European Journal of Inorganic Chemistry
12. CatalySeed: A Reaction Database for Ruthenium-Catalyzed Ethenolysis of Seed Oils with Applications in Machine Learning
    Albert Poater, Susana García‐Abellán, Juan V. Alegre‐Requena, Bartosz Trzaskowski, Juan Pablo Martínez · 2026 · ACS Catalysis
13. Machine Learning Parameters of Optimally Tuned Range-Separated Hybrid Functionals for Transition Metal Complexes
    Xiang‐Yang Liu, Qing-Xin Xiang, Sheng-Rui Wang, Dongyi Xiao, Wei‐Hai Fang, Ganglong Cui · 2025 · The Journal of Physical Chemistry Letters
14. Iron-Catalyzed Kumada Cross-Coupling Reaction Involving Fe8Me12– and Related Clusters: A Computational Study
    Andrea Darù, Carlos Martín−Fernández, Jeremy Noel Harvey · 2022 · ACS Catalysis
15. Structure-Based Relative Energy Prediction Model: A Case Study of Pd(II)-Catalyzed Ethylene Polymerization and the Electronic Effect of Ancillary Ligands
    Lu Han, Xiaohui Kang, Yi Luo · 2021 · The Journal of Physical Chemistry B
16. Machine Learning Models for Predicting Zirconocene Properties and Barriers
    Justin Kyle Kirkland, Jugal Kumawat, Maliheh Shaban Tameh, Tyson Tolman, Allison Lambert, G.R. Lief, Qing Yang, Daniel H. Ess · 2024 · Journal of Chemical Information and Modeling
17. Machine‐Learning‐Based Design of Metallocene Catalysts for Controlled Olefin Copolymerization
    Yongjun Kim, Yeonjoon Kim, Hyeonsu Kim, Sungwoo Kang, Jaewook Kim, Kyung-Hoon Lee, Wook Jeong, Won Jong Lee, Ho Ryu, Kyungwoo Kim et al. · 2025 · Chemistry - A European Journal
18. tmQMg* Data Set: Excited State Properties of 74k Transition Metal Complexes
    Hannes Kneiding, David Balcells · 2025 · Journal of Chemical Information and Modeling
19. Cost-Effective Quantum-Mechanical Workflows for Molecular Machine Learning
    David Dalmau, Lauriane Jacot‐Descombes, Adarsh V. Kalikadien, Brenda Manzanilla, Evgeny A. Pidko, Kjell Jorner, Matthew S. Sigman, Juan V. Alegre-Requena · 2026 · ACS Catalysis
20. Predicting hydrogen atom transfer energy barriers using Gaussian process regression
    Evgeni Ulanov, Ghulam A. Qadir, Kai Riedmiller, Pascal Friederich, Frauke Gräter · 2025 · Digital Discovery
21. Machine Learning in Predicting Activation Barrier Energy of C=N Bond Rotation in Amides
    Michele Tomasini, Michal Szostak, Albert Poater · 2025 · Asian Journal of Organic Chemistry
22. High-Throughput Optimization of a High-Pressure Catalytic Reaction
    Yusuke Tanabe, Hiroki Sugisawa, Tomohisa Miyazawa, Kazuhiro Hotta, Kazuya Shiratori, Tadahiro Fujitani · 2025 · Journal of Chemical Information and Modeling
23. Specification Search in Structural Equation Modeling (SEM): How Gradient Component-wise Boosting can Contribute
    Bjørn Gunnar Hansen, Ulf Henning Olsson · 2021 · Structural Equation Modeling A Multidisciplinary Journal
24. Co-intelligent Design of Catalysis Research with Large Language Models: Hype or Reality?
    David Balcells · 2025 · ACS Catalysis
25. Enabling Automation of de Novo Catalyst Design: An Experimentally Validated, Multifactor Design Metric for Olefin Metathesis
    Jonas B. Ekeli, Marco Foscato, Christian O. Blanco, Giovanni Occhipinti, Deryn E. Fogg, Vidar Remi Jensen · 2024 · ACS Catalysis
26. Toward AI/ML-assisted discovery of transition metal complexes
    Hongni Jin, Kenneth M. Merz · 2024 · Annual reports in computational chemistry
27. Estimation of the Surface Free Energy Components for Solid Surfaces: A Machine Learning Approach
    Ebubekir Sıddık Aydın, Ibrahim Korkut, Salih Ozbay · 2023 · Arabian Journal for Science and Engineering
28. MetalloGen: Automated 3D Conformer Generation for Diverse Coordination Complexes
    Kyung-Hoon Lee, Shinyoung Park, Mi Jeong Park, Woo Youn Kim · 2025 · Journal of Chemical Information and Modeling
29. Rapid Generation of Transition-State Conformer Ensembles via Constrained Distance Geometry
    Stefan P. Schmid, Henrik Seng, Thibault Kläy, Kjell Jorner · 2026 · Journal of Chemical Information and Modeling
30. Staged Diversity‐Constrained Machine Learning for High‐Dimensional Reaction Condition Optimization
    Shu‐Wen Li, Shan Chen, João C. A. Oliveira, Shimin Zhang, Lutz Ackermann, Xin Hong · 2026 · Angewandte Chemie International Edition
31. Toward Reaction Vessel Mimicry: Machine Learning-Assisted Automated Exploration of Alkene Polymerization and Its Transferability
    Sagar Ghorai, Ruben Staub, Yu Harabuchi, Takashi Nakano, Alexandre A. Varnek, Satoshi Maeda · 2026 · Journal of Chemical Theory and Computation
32. Deep Graph Learning for Molecules and Materials
    Hannes Kneiding, David Balcells · 2023 · Nordic Machine Intelligence
33. Machine-learning models for atom-diatom reactions across isotopologues
    Daniel Julian, Rian Koots, Jesús Pérez‐Ríos · 2024 · Physical Review A
34. Unveiling the impact of ligand configurations and structural fluxionality on virtual screening of transition-metal complexes
    Adarsh V. Kalikadien, Niels J. van der Lem, Cecile Valsecchi, Laurent Lefort, Evgeny A. Pidko · 2025 · Digital Discovery
35. Improving generative inverse design of molecular catalysts in small data regime
    François Cornet, Pratham Deshmukh, Bardi Benediktsson, Mikkel Nørgaard Schmidt, Arghya Bhowmik · 2025 · Machine Learning Science and Technology
36. 3DReact: Geometric Deep Learning for Chemical Reactions
    Puck van Gerwen, Ksenia R. Briling, Charlotte Bunne, Vignesh Ram Somnath, Rubén Laplaza, Andreas Krause, Clémence Corminbœuf · 2023 · arXiv (Cornell University)
37. Integrated Study on Methane Activation: Exploring Main Group Frustrated Lewis Pairs through Density Functional Theory, Machine Learning, and Machine-Learned Force Fields
    Ignacio Migliaro, Thomas R. Cundari · 2024 · Journal of Chemical Theory and Computation
38. Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions
    Guo‐Jin Cao · 2025 · International Journal of Quantum Chemistry
39. Toward on-the-fly prediction of reaction energetics for high-throughput screening
    Jasper De Landsheere, Maximilian P.-P. Kovar, Konstantin Mark, Leon Ganser, Leonard Galustian, Johannes Karwounopoulos, Charlotte Gerhaher, Esther Heid · 2026 · Chemical Physics Reviews
40. From the Felkin‐Anh Rule to the Grignard Reaction: an Almost Circular 50 Year Adventure in the World of Molecular Structures and Reaction Mechanisms with Computational Chemistry**
    Odile Eisenstein · 2022 · Israel Journal of Chemistry
41. On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing a Computational Data Set
    Hiroaki Okada, Satoshi Maeda · 2024 · ACS Omega
42. ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design
    Daniel H. Ess, Shusen Chen, Zack Meyer, Brendan Jensen, Alex Kraus, Allison Lambert · 2023 · ChemRxiv
43. Computational Catalyst Design with Data–Driven Tools – General Approaches and Applications
    Jolene P. Reid · 2022 · Elsevier eBooks
44. Incorporating Anionic Ligands in Chemical Space Exploration with New Ligand Additivity Relationships
    Heather J. Kulik · 2025 · Journal of Chemical Information and Modeling
45. Deconvoluting the Effects of Substituents on Reaction Barriers through Machine Learning: The Case of Brønsted Acid-Mediated Nazarov Cyclizations
    Angus B. Keto, Elizabeth H. Krenske · 2025 · The Journal of Organic Chemistry
46. Machine learning prediction of a chemical reaction over 8 decades of energy
    Daniel Julian, Jesús Pérez‐Ríos · 2025 · Physical Review Research
47. Insight into the temperature-dependent lattice thermal properties of CeO 2 stabilized ZrO 2 by machine learning force fields
    Linpo Yang, Jia Wei, Yinglin Song · 2025 · Physical Chemistry Chemical Physics
48. Effects of phase transitions and oxygen vacancies on thermal transport in yttrium-stabilized zirconia studied using neural network potentials
    Zifu Zang, Yushun Zhao, Chao Sui, Shaodong Sun, 鄭凱伊, Renjie Ding, Yongqiang Tao, Xiaodong He, Chao Wang · 2026 · Physical review. B./Physical review. B
49. 3DTMC-LLM: A 3D Geometry-Aware Large Language Model for Transition Metal Complexes
    J ZHU, Farshad Shiri, Liren Xiao, Yizhou Wang, Haibin Su, Zhenyang Lin · 2026 · Journal of Chemical Information and Modeling
50. Evolutionary Algorithms and Workflows for De Novo Catalyst Design
    Marco Foscato, Jonas B. Ekeli, Marcello Costamagna, D. Grellscheid, Vidar Remi Jensen · 2023 · Elsevier eBooks
51. Molecular modelling of encapsulation and reactivity within metal-organic cages (MOCs)
    Mercè Alemany‐Chavarria, Gantulga Norjmaa, Giuseppe Sciortino, Gregori Ujaque · 2024 · Advances in catalysis
52. ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold
    Adarsh V. Kalikadien, Evgeny A. Pidko, Vivek Sinha · 2021 · ChemRxiv
53. ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold
    Adarsh V. Kalikadien, Evgeny A. Pidko, Vivek Sinha · 2021 · ChemRxiv
54. Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces
    Shi Jun Ang, Wujie Wang, Daniel Schwalbe‐Koda, Simon Axelrod, Rafael Gómez‐Bombarelli · 2020 · ChemRxiv
55. Evolutionary Multiobjective Optimization of Multiligand Metal Complexes in Diverse and Vast Chemical Spaces
    Hannes Kneiding, Ainara Nova, David Balcells · 2023 · ChemRxiv
56. Prediction of Reaction Orthogonality using Machine Learning
    Hootan Roshandel, Santiago Vargas, Amy J. Lai, Anastassia N. Alexandrova, Paula L. Diaconescu · 2023 · ChemRxiv
57. Discovery of Hemilabile Ligands Using MachineLearning
    Ilia Kevlishvili, Chenru Duan, Heather J. Kulik · 2023 · ChemRxiv
58. Predicting Reactivity with Machine Learning
    Lauriane Jacot‐Descombes, Kjell Jorner · 2025 · Wiley eBooks
59. TMtopo Dataset — Quantum Geometries and Density Topology for 1.1k Transition Metal Complexes
    Filipe Teixeira, Edgar Silva-Santos, M. Natália D. S. Cordeiro · 2021 · ChemRxiv
60. ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold
    Adarsh V. Kalikadien, Evgeny A. Pidko, Vivek Sinha · 2021 · ChemRxiv
61. Computational Methods for Training Set Selection and Error Assessment Applied to Catalyst Design: Guidelines for Deciding Which Reactions to Run First and Which to Run Next
    Scott E. Denmark, Andrew F. Zahrt, William T. Darrow, Brennan T. Rose, Jeremy J. Henle · 2020 · ChemRxiv
62. TMtopo Dataset — Quantum Geometries and Density Topology for 1.1k Transition Metal Complexes
    Filipe B. Teixeira, Edgar Silva-Santos, M. Natália D. S. Cordeiro · 2021 · ChemRxiv
63. ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold
    Adarsh V. Kalikadien, Evgeny A. Pidko, Vivek Sinha · 2021 · ChemRxiv
64. Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks
    Jon Paul Janet · 2022 · ACS symposium series
65. AABBA: Atom–Atom Bond–Bond Bond–Atom Graph Kernel for Machine Learning on Molecules and Materials
    Lucía Morán‐González, Jørn Eirik Betten, Hannes Kneiding, David Balcells · 2023 · ChemRxiv
66. Tailoring phosphine ligands for improved C–H activation: insights from Δ-machine learning
    Tianbai Huang, Robert Geitner, Alexander Croy, Stefanie Gräfe · 2024 · Digital Discovery
67. Every atom counts: predicting sites of reaction based on chemistry within two bonds
    Ching Ching Lam, Jonathan M. Goodman · 2024 · Digital Discovery
68. Construction of Training Datasets for Chemical Reactivity Prediction Through Computational Means
    Thijs Stuyver, Javier Emilio Alfonso Ramos · 2025 · Wiley eBooks
69. Optimization of Catalysts Using Computational Chemistry, Machine Learning, and Cheminformatics
    David Dalmau, Juan V. Alegre‐Requena · 2025 · Wiley eBooks
70. Navigating Chemical Space: Pairing Quantum Mechanics with Machine Learning Predictions for Catalysis Design
    Ignacio Migliaro · 2024
71. Mechanism-informed machine learning for rational catalyst design: application to regioselectivity of allyl acetate hydroformylation
    Minhua Zhang, Yuzhe Sun, Yifei Chen, Lingtao Wang, Kai Song, Hao Gong · 2025 · Catalysis Science & Technology
72. Stufenweises diversitätsbeschränktes maschinelles Lernen für die Optimierung hochdimensionaler Reaktionsbedingungen
    Shu‐Wen Li, Shan Chen, João C. A. Oliveira, Shimin Zhang, Lutz Ackermann, Xin Hong · 2026 · Angewandte Chemie
73. Multiobjective Optimization of Synthetically Accessible Homogeneous Catalysts Reveals Tradeoffs for Methane-to-Methanol Conversion
    Husain Adamji, Ilia Kevlishvili, David W. Kastner, Ralf Meyer, Yuriy Román‐Leshkov, Heather J. Kulik · 2026 · ACS Catalysis
74. Data-driven modelling of Hoveyda–Grubbs-II-type olefin metathesis precatalyst initiation: mechanistic insights and predictive models
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75. Δ‐Machine Learning for the Prediction of Metal Complex Properties
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76. 3D, Structure-Based, Deep Learning Approach to Predict the Regioselectivity of Olefin Hydroformylations
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77. Towards sustainable exploration of chemical spaces with machine learning
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78. Catalyst‐Specialized Chemical Language Model Based on Transformer Variational Autoencoder for Catalyst Design and Discovery
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79. Predicting the Reactivity of Acyclic Silylenes and Germylenes in Hydrogen Activation Using Machine Learning
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