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Results: 13

Miguel Martinez‐Fernandez, MdBin Yeamin, David Dalmau, Jorge J. Carbó, Albert Poater, Juan V. Alegre‐Requena
Data‐Driven Analysis of Ni‐Catalyzed Semihydrogenations of Alkynes
Adv Synth Catal, 2025, [], ASAP-
DOI: 10.1002/adsc.202401444
OpenAccess: –
Keywords: Computational chemistry, Homogeneous catalysis, Machine learning, Predictive Chemistry, Reaction mechanisms

Michele Tomasini, Michal Szostak, Albert Poater
Machine Learning in Predicting Activation Barrier Energy of C=N Bond Rotation in Amides
Asian J Org Chem, 2025, 14, e202400749
DOI: 10.1002/ajoc.202400749
OpenAccess: Link
Keywords: Computational chemistry, Machine learning, Organometallics, Predictive Chemistry, Reaction mechanisms

Zhen Cao, Laura Falivene, Albert Poater, Bholanath Maity, Ziyung Zhang, Gentoku Takasao, SadeedBin Sayed, Andrea Petta, Giovanni Talarico, Romina Oliva, Luigi Cavallo
COBRA web application to benchmark linear regression models for catalyst optimization with few-entry datasets
Cell Reports Physical Science, 2025, 6, 102348-
DOI: 10.1016/j.xcrp.2024.102348
OpenAccess: –
Keywords: Chemical bonding, Computational chemistry, Joint Exp-Comp, Machine learning, Predictive Chemistry

Sergei F. Vyboishchikov
Atomic Neural Network for Calculation of Solvation Free Energies in Organic Solvents
J Comput Chem, 2025, 46, e70104
DOI: 10.1002/jcc.70104
OpenAccess: –
Keywords: Machine learning, Method development

Evert Jan Baerends, Nestor F. Aguirre, Nick D. Austin, Jochen Autschbach, F. Matthias Bickelhaupt, Rosa Bulo, Chiara Cappelli, Adri C. T. van Duin, Franco Egidi, Célia Fonseca Guerra, Arno Förster, Mirko Franchini, Theodorus P. M. Goumans, Thomas Heine, Matti Hellström, Christoph R. Jacob, Lasse Jensen, Mykhaylo Krykunov, Erik van Lenthe, Artur Michalak, Mariusz M. Mitoraj, Johannes Neugebauer, Valentin Paul Nicu, Pier Philipsen, Harry Ramanantoanina, Robert Rüger, Georg Schreckenbach, Mauro Stener, Marcel Swart, Jos M. Thijssen, Tomás Trnka, Lucas Visscher, Alexei Yakovlev, Stan van Gisbergen
The Amsterdam Modeling Suite
J. Chem. Phys., 2025, 162, 162501
DOI: 10.1063/5.0258496
OpenAccess: Link
Keywords: Computational chemistry, Machine learning, Method development, Spectroscopy

Michele Tomasini, Maria Voccia, Lucia Caporaso, Michal Szostak, Albert Poater
Tuning the steric hindrance of alkylamines: a predictive model of steric editing of planar amines
Chem. Sci., 2024, 15, 13405-13414
DOI: 10.1039/D4SC03873H
OpenAccess: Link
Keywords: Chemical bonding, Computational chemistry, Cross-coupling reactions, Machine learning, Organometallics

Sílvia Escayola, Naeimeh Bahri-Laleh, Albert Poater
%V _Bur index and steric maps: from predictive catalysis to machine learning
Chem. Soc. Rev., 2024, 53, 853-882
DOI: 10.1039/D3CS00725A
OpenAccess: –
Keywords: Chemical bonding, Computational chemistry, Machine learning, Predictive Chemistry, Sustainable Catalysis

Sergei F. Vyboishchikov
Solvation Enthalpies and Free Energies for Organic Solvents through a Dense Neural Network: A Generalized-Born Approach
Liquids, 2024, 4, 525-538
DOI: 10.3390/liquids4030030
OpenAccess: Link
Keywords: Machine learning, Method development

Sergei F. Vyboishchikov
Dense Neural Network for Calculating Solvation Free Energies from Electronegativity-Equalization Atomic Charges
J. Chem. Inf. Model., 2023, 63, 6283-6292
DOI: 10.1021/acs.jcim.3c00922
OpenAccess: –
Keywords: Machine learning, Method development

Elizaveta F. Petrusevich, Manon H. E. Bousquet, Borys Ośmiałowski, Denis Jacquemin, Josep M. Luis, Robert Zaleśny
Cost-Effective Simulations of Vibrationally-Resolved Absorption Spectra of Fluorophores with Machine-Learning-Based Inhomogeneous Broadening
J. Chem. Theory Comput., 2023, 19, 2304-2315
DOI: 10.1021/acs.jctc.2c01285
OpenAccess: Link
Keywords: Computational chemistry, Excited states, Machine learning, Method development, Spectroscopy