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Results: 10

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, [], ASAP-
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

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

Sergei F. Vyboishchikov
Predicting Solvation Free Energies Using Electronegativity-Equalization Atomic Charges and a Dense Neural Network: A Generalized-Born Approach
J. Chem. Theory Comput., 2023, 19, 8340-8350
DOI: 10.1021/acs.jctc.3c00858
OpenAccess: –
Keywords: Machine learning, Method development

Roger Monreal-Corona, Anna Pla-Quintana, Albert Poater
Predictive catalysis: a valuable step towards machine learning
Trends in Chemistry, 2023, 5, 935-946
DOI: 10.1016/j.trechm.2023.10.005
OpenAccess: –
Keywords: Chemical bonding, Machine learning, Predictive Chemistry, Reaction mechanisms, Sustainable Catalysis

Daniel Bosch, Jun Wang, Lluís Blancafort
Fingerprint-based deep neural networks can model thermodynamic and optical properties of eumelanin DHI dimers
Chem. Sci., 2022, 13, 8942-8946
DOI: 10.1039/D2SC02461F
OpenAccess: Link
Keywords: Computational chemistry, Density Functional Theory, Excited states, Machine learning, Melanin