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Results: 1753

Folorunsho Bright Omage, Andrea Madabeni, Amanda Resende Tucci, Pablo Andrei Nogara, Marco Bortoli, Alicedos Santos Rosa, Vivian Neuza dos Santos Ferreira, João Batista Teixeira Rocha, Milene Dias Miranda, Laura Orian
Diphenyl Diselenide and SARS-CoV-2:in silico Exploration of the Mechanisms of Inhibition of Main Protease (Mpro ) and Papain-like Protease (PLpro )
J. Chem. Inf. Model., 2023, 63, 2226-2239
DOI: 10.1021/acs.jcim.3c00168
Keywords: Computational chemistry

SergeiF. 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

Martí Gimferrer, Pedro Salvador
Exact decompositions of the total KS-DFT exchange–correlation energy into one- and two-center terms
J. Chem. Phys, 2023, 158, 234105
DOI: 10.1063/5.0142778
Keywords: Chemical bonding, Method development, Real-space analysis

Pau Besalú-Sala, Alexander A. Voityuk, Josep M. Luis, Miquel Solà
Effect of external electric fields in the charge transfer rates of donor–acceptor dyads: A straightforward computational evaluation
J. Chem. Phys, 2023, 158, 244111
DOI: 10.1063/5.0148941
Keywords: Chemical bonding, Electron and energy transfer, Excited states, Method development, Photovoltaic materials

Carmelo Naim, Pau Besalú-Sala, Robert Zaleśny, Josep M. Luis, Frédéric Castet, Eduard Matito
Are Accelerated and Enhanced Wave Function Methods Accurate to Compute Static Linear and Nonlinear Optical Properties?
J. Chem. Theory Comput., 2023, 19, 1753-1764
DOI: 10.1021/acs.jctc.2c01212
Keywords: Computational chemistry, Nonlinear optical properties, Spectroscopy

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

Martí Gimferrer, Sergi Danés, Diego M. Andrada, Pedro Salvador
Merging the Energy Decomposition Analysis with the Interacting Quantum Atoms Approach
J. Chem. Theory Comput., 2023, 19, 3469–3485
DOI: 10.1021/acs.jctc.3c00143
Keywords: Chemical bonding, Method development, Real-space analysis

Pau Besalú-Sala, Fabien Bruneval, ÁngelJosé Pérez-Jiménez, JuanCarlos Sancho-García, Mauricio Rodríguez-Mayorga
RPA, an Accurate and Fast Method for the Computation of Static Nonlinear Optical Properties
J. Chem. Theory Comput., 2023, 19, 6062-6069
DOI: 10.1021/acs.jctc.3c00674
Keywords: Computational chemistry, Method development, Nonlinear optical properties

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

Shiyi Yang, Xiang Yu, Yaxu Liu, Michele Tomasini, Lucia Caporaso, Albert Poater, Luigi Cavallo, CatherineS.J. Cazin, StevenP. Nolan, Michal Szostak
Suzuki–Miyaura Cross-Coupling of Amides by N–C Cleavage Mediated by Air-Stable, Well-Defined [Pd(NHC)(sulfide)Cl] Catalysts: Reaction Development, Scope, and Mechanism
J. Org. Chem., 2023, 88, 10858-10868
DOI: 10.1021/acs.joc.3c00912
Keywords: Computational chemistry, Cross-coupling reactions, Homogeneous catalysis, Organometallics, Reaction mechanisms

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