Éric Brémond

Associate Professor

Université Paris-Saclay

France

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About

Éric Brémond is an Associate Professor at Université de Paris, France. His research primarily focuses on visible-light-driven hydrogen production, pericyclic reactions, and intermolecular interactions, with recent publications highlighting advancements in density functional theory and machine learning techniques. Additionally, he explores carbon–carbon bonding for electron storage and the modeling of infrared and Raman spectra.

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2022

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2022

Collaborators (18)

Amit Kumar

University of St. Andrews

UNITED KINGDOM

Éric Brémond

Research Interests

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About

Articles (27)

Efficient Visible-Light-Driven Hydrogen Production in Aqueous Media by the Association of the Triazatriangulenium (TATA+) Dye and the DuBois’ Nickel Catalyst

CYCLO70: A New Challenging Pericyclic Benchmarking Set for Kinetics and Thermochemistry Evaluation

Fast and Reliable Evaluation of Intermolecular Interactions with Second-Order Møller–Plesset Perturbation Theory and Small Basis Set

The Structronic Concept: Harnessing Carbon─Carbon Chemical Bonding for Electron Storage and Related Applications

Improving the Reliability of, and Confidence in, DFT Functional Benchmarking through Active Learning

Are Redox‐Active Centers Bridged by Saturated Flexible Linkers Systematically Electrochemically Independent?

Are Redox‐Active Centers Bridged by Saturated Flexible Linkers Systematically Electrochemically Independent?

Innentitelbild: Are Redox‐Active Centers Bridged by Saturated Flexible Linkers Systematically Electrochemically Independent? (Angew. Chem. 31/2024)

Inside Cover: Are Redox‐Active Centers Bridged by Saturated Flexible Linkers Systematically Electrochemically Independent? (Angew. Chem. Int. Ed. 31/2024)

Comment on “Dispersion-corrected r2SCAN based double-hybrid functionals” [J. Chem. Phys. 159, 224103 (2023)]

Axial–equatorial equilibrium in substituted cyclohexanes: a DFT perspective on a small but complex problem

Predicting redox potentials by graph‐based machine learning methods

Preface of Carlo Adamo's virtual special issue

Boosting the Modeling of Infrared and Raman Spectra of Bulk Phase Chromophores with Machine Learning

Evaluating Noncovalent Interactions in Halogenated Molecules with Double-Hybrid Functionals and a Dedicated Small Basis Set

Study of Sterically Crowded Alkanes: Assessment of Non‐Empirical Density Functionals Including Double‐Hybrid (Cost‐Effective) Methods

Searching the Best Double-Hybrid Density Functional to Correctly Predict the Singlet–Triplet Excited-State Inversion in Organic Systems

Predicting ruthenium catalysed hydrogenation of esters using machine learning

DockSurf: A Molecular Modeling Software for the Prediction of Protein/Surface Adhesion

Modeling the Photo-Absorption Properties of Noble Metal Nanoclusters: A Challenge for Density-Functional Theory

Excitation energies of polycylic aromatic hydrocarbons by double-hybrid functionals: Assessing the PBE0-DH and PBE-QIDH models and their range-separated versions

Concerted versus stepwise proton transfer reactions in the [2, 2′‐bipyridyl]‐3‐3′‐diol molecule: A static and dynamic ab‐initio investigation

Violation of Hund’s rule in molecules: Predicting the excited-state energy inversion by TD-DFT with double-hybrid methods

Non-empirical double-hybrid density functionals as reliable tools for electronic structure calculations

Tackling an accurate description of molecular reactivity with double-hybrid density functionals

Double Hybrids and Noncovalent Interactions: How Far Can We Go?

Free Energy Profiles of Proton Transfer Reactions: Density Functional Benchmark from Biased Ab Initio Dynamics

Collaborators (18)

Amit Kumar

Philippe P. Lainé

Nadia REGA

Pierre Tufféry

Gianni Cardini

Florent Barbault

Romana Sokolova

Gaetano Ricci

Marika Savarese

Niklas von Wolff

Yoann Olivier

Carlo Adamo

François Maurel

Minh-Huong Ha-Thi

Eva Vaněčková

Ángel J. Pérez-Jiménez

Ilaria Ciofini

Frédéric Labat

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