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Results: 6

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

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
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
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
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
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
Keywords: Computational chemistry, Density Functional Theory, Excited states, Machine learning, Melanin



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