Papers citing Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex
Discovery of Ni(I) Complexes for CO2 Insertion Enabled by a Machine Learning-Computational-Selection Sequence
Journal of the American Chemical Society · 2025
Julian A. Hueffel, Mathilde Rigoulet, Sebastian Wellig, Theresa Sperger, Jas S. Ward, Kari T. Rissanen, Franziska Schoenebeck
Read original paper ↗ ·CaTS: Toward Scalable and Efficient Transition State Screening for Catalyst Discovery
ACS Catalysis · 2025
Jun Yin, Wentao Li, Honghao Chen, Ju Qiu, Huasheng Feng, Xiangya Xu, Qiuyan Jin, Xiaonan Wang
Read original paper ↗ ·Machine Learning Parameters of Optimally Tuned Range-Separated Hybrid Functionals for Transition Metal Complexes
The Journal of Physical Chemistry Letters · 2025
Xiang‐Yang Liu, Qing-Xin Xiang, Sheng-Rui Wang, Dongyi Xiao, Wei‐Hai Fang, Ganglong Cui
Read original paper ↗ ·Co-intelligent Design of Catalysis Research with Large Language Models: Hype or Reality?
ACS Catalysis · 2025
David Balcells
Read original paper ↗ ·tmQMg* Data Set: Excited State Properties of 74k Transition Metal Complexes
Journal of Chemical Information and Modeling · 2025
Hannes Kneiding, David Balcells
Read original paper ↗ ·MetalloGen: Automated 3D Conformer Generation for Diverse Coordination Complexes
Journal of Chemical Information and Modeling · 2025
Kyung-Hoon Lee, Shinyoung Park, Mi Jeong Park, Woo Youn Kim
Read original paper ↗ ·Machine learning prediction of a chemical reaction over 8 decades of energy
Physical Review Research · 2025
Daniel Julian, Jesús Pérez‐Ríos
Read original paper ↗ ·On the use of chemical bonding descriptors in machine learning
Coordination Chemistry Reviews · 2025
Michele Tomasini, Lucia Caporaso, Martí Gimferrer, Albert Poater
Read original paper ↗ ·Insight into the temperature-dependent lattice thermal properties of CeO 2 stabilized ZrO 2 by machine learning force fields
Physical Chemistry Chemical Physics · 2025
Linpo Yang, Jia Wei, Yinglin Song
Read original paper ↗ ·Machine Learning in Homogeneous Catalysis: Basic Concepts and Best Practices
ACS Catalysis · 2025
David Dalmau, Susana García-Abellán, Juan V. Alegre-Requena
Read original paper ↗ ·Mechanism-informed machine learning for rational catalyst design: application to regioselectivity of allyl acetate hydroformylation
Catalysis Science & Technology · 2025
Minhua Zhang, Yuzhe Sun, Yifei Chen, Lingtao Wang, Kai Song, Hao Gong
Read original paper ↗ ·Data-driven modelling of Hoveyda–Grubbs-II-type olefin metathesis precatalyst initiation: mechanistic insights and predictive models
Catalysis Science & Technology · 2026
Henning Remm, Viktoria H. Gessner, Tobias Gensch
Read original paper ↗ ·Towards sustainable exploration of chemical spaces with machine learning
Digital Discovery · 2026
Leonardo Rafael Medrano Sandonas, David Balcells, Tom Barnowsky, Anton S. Bochkarev, Jacqueline Cole, Volker L. Deringer, Werner Dobrautz, Adrian Ehrenhofer, Thorben Frank, Pascal Friederich, Rico Friedrich, Janine George, Luca M. Ghiringhelli, Alejandra Hinostroza Caldas, Veronika Jurásková, Hannes Kneiding, Yury Lysogorskiy, Johannes T. Margraf, Hanna Türk, O. Anatole von Lilienfield, Milica Todorović, Alexandre Tkatchenko, Mariana Rossi, Gianaurelio Cuniberti
Read original paper ↗ ·CatalySeed: A Reaction Database for Ruthenium-Catalyzed Ethenolysis of Seed Oils with Applications in Machine Learning
ACS Catalysis · 2026
Albert Poater, Susana García‐Abellán, Juan V. Alegre‐Requena, Bartosz Trzaskowski, Juan Pablo Martínez
Read original paper ↗ ·Rapid Generation of Transition-State Conformer Ensembles via Constrained Distance Geometry
Journal of Chemical Information and Modeling · 2026
Stefan P. Schmid, Henrik Seng, Thibault Kläy, Kjell Jorner
Read original paper ↗ ·Staged Diversity‐Constrained Machine Learning for High‐Dimensional Reaction Condition Optimization
Angewandte Chemie International Edition · 2026
Shu‐Wen Li, Shan Chen, João C. A. Oliveira, Shimin Zhang, Lutz Ackermann, Xin Hong
Read original paper ↗ ·Stufenweises diversitätsbeschränktes maschinelles Lernen für die Optimierung hochdimensionaler Reaktionsbedingungen
Angewandte Chemie · 2026
Shu‐Wen Li, Shan Chen, João C. A. Oliveira, Shimin Zhang, Lutz Ackermann, Xin Hong
Read original paper ↗ ·molSimplify 2.0: Improved Structure Generation for Automating Discovery in Inorganic Molecular and Reticular Chemistry
Journal of Chemical Information and Modeling · 2026
Gianmarco Terrones, Roland G. St. Michel, Jacob Toney, Akash Ball, Yiran Wang, Aaron G. Garrison, Aditya Nandy, Ralf Meyer, Freya Edholm, Changhwan Oh, Sebastian G. Pujet, Daniel B. K. Chu, Davut Muhammetgulyyev, Heather J. Kulik
Read original paper ↗ ·Toward Reaction Vessel Mimicry: Machine Learning-Assisted Automated Exploration of Alkene Polymerization and Its Transferability
Journal of Chemical Theory and Computation · 2026
Sagar Ghorai, Ruben Staub, Yu Harabuchi, Takashi Nakano, Alexandre A. Varnek, Satoshi Maeda
Read original paper ↗ ·Effects of phase transitions and oxygen vacancies on thermal transport in yttrium-stabilized zirconia studied using neural network potentials
Physical review. B./Physical review. B · 2026
Zifu Zang, Yushun Zhao, Chao Sui, Shaodong Sun, 鄭凱伊, Renjie Ding, Yongqiang Tao, Xiaodong He, Chao Wang
Read original paper ↗ ·Cost-Effective Quantum-Mechanical Workflows for Molecular Machine Learning
ACS Catalysis · 2026
David Dalmau, Lauriane Jacot‐Descombes, Adarsh V. Kalikadien, Brenda Manzanilla, Evgeny A. Pidko, Kjell Jorner, Matthew S. Sigman, Juan V. Alegre-Requena
Read original paper ↗ ·Toward on-the-fly prediction of reaction energetics for high-throughput screening
Chemical Physics Reviews · 2026
Jasper De Landsheere, Maximilian P.-P. Kovar, Konstantin Mark, Leon Ganser, Leonard Galustian, Johannes Karwounopoulos, Charlotte Gerhaher, Esther Heid
Read original paper ↗ ·3DTMC-LLM: A 3D Geometry-Aware Large Language Model for Transition Metal Complexes
Journal of Chemical Information and Modeling · 2026
J ZHU, Farshad Shiri, Liren Xiao, Yizhou Wang, Haibin Su, Zhenyang Lin
Read original paper ↗ ·Δ‐Machine Learning for the Prediction of Metal Complex Properties
Chemistry - A European Journal · 2026
Hannes Kneiding, David Balcells
Read original paper ↗ ·3D, Structure-Based, Deep Learning Approach to Predict the Regioselectivity of Olefin Hydroformylations
Journal of the American Chemical Society · 2026
Nicholas Hadler, N. Ian Rinehart, Masha Elkin, Jeremy Nicolai, Golsa Gheibi, Jiaqing Chen, Matthew Avaylon, Ross Maciejewski, Gunther H. Weber, Michael W. Mahoney, Talita Perciano, John F. Hartwig
Read original paper ↗ ·Catalyst‐Specialized Chemical Language Model Based on Transformer Variational Autoencoder for Catalyst Design and Discovery
Advanced Intelligent Discovery · 2026
Apakorn Kengkanna, Masahito Ohue
Read original paper ↗ ·Predicting the Reactivity of Acyclic Silylenes and Germylenes in Hydrogen Activation Using Machine Learning
Advanced Science · 2026
Michelle Kleinhaus, Henning Remm, Prasenjit Palui, Alexander Linke, Viktoria H. Gessner
Read original paper ↗ ·