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Results: 8

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