Riccardo Conte

Assistant Professor of Theoretical Chemistry

Università degli Studi di Torino

Italy

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About

Riccardo Conte is an Assistant Professor of Theoretical Chemistry at Università degli Studi di Milano, Italy. His research areas focus on the application of machine learning in chemistry, nuclear quantum effects in molecular systems, and semiclassical approaches to spectroscopy and kinetics. Recent articles include work on machine-learned potentials and the vibrational spectra of complex molecular systems.

Articles (24)

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2022

Collaborators (6)

Benedetta Mennucci

Professor

University of Pisa

ITALY

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Joel M. Bowman

Emory University

UNITED STATES

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Stephen G. Kukolich

University of Arizona

UNITED STATES

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Qi Yu

Yale University

UNITED STATES

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David R. Yarkony

Johns Hopkins University

UNITED STATES

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Michele Ceotto

Associate Professor

Università degli Studi di Torino

ITALY

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Riccardo Conte

Research Interests

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About

Articles (24)

Extending atomic decomposition and many-body representation with a chemistry-motivated approach to machine learning potentials

Semiclassical description of nuclear quantum effects in solvated and condensed phase molecular systems

A New <i>A Priori</i> Method to Avoid Calculation of Negligible Hamiltonian Matrix Elements in CI Calculation

Formic Acid–Ammonia Heterodimer: A New Δ-Machine Learning CCSD(T)-Level Potential Energy Surface Allows Investigation of the Double Proton Transfer

A Perspective on the Investigation of Spectroscopy and Kinetics of Complex Molecular Systems with Semiclassical Approaches

A time averaged semiclassical approach to IR spectroscopy

Assessing Permutationally Invariant Polynomial and Symmetric Gradient Domain Machine Learning Potential Energy Surfaces for H3O2–

Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine

No Headache for PIPs: A PIP Potential for Aspirin Runs Much Faster and with Similar Precision Than Other Machine-Learned Potentials

Unraveling Water Solvation Effects with Quantum Mechanics/Molecular Mechanics Semiclassical Vibrational Spectroscopy: The Case of Thymidine

Ab Initio Potential Energy Surface for NaCl–H2 with Correct Long-Range Behavior

Dynamics Calculations of the Flexibility and Vibrational Spectrum of the Linear Alkane C14H30, Based on Machine-Learned Potentials

Δ-Machine Learning to Elevate DFT-Based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol

Machine learning classification can significantly reduce the cost of calculating the Hamiltonian matrix in CI calculations

Anharmonic Assignment of the Water Octamer Spectrum in the OH Stretch Region

Interfacing q-AQUA with a Polarizable Force Field: The Best of Both Worlds

Ring-Polymer Instanton Tunneling Splittings of Tropolone and Isotopomers using a Δ-Machine Learned CCSD(T) Potential: Theory and Experiment Shake Hands

PESPIP: Software to fit complex molecular and many-body potential energy surfaces with permutationally invariant polynomials

Δ-Machine Learned Potential Energy Surfaces and Force Fields

Investigating the Spectroscopy of the Gas Phase Guanine–Cytosine Pair: Keto versus Enol Configurations

Semiclassical and VSCF/VCI Calculations of the Vibrational Energies of trans- and gauche-Ethanol Using a CCSD(T) Potential Energy Surface

Nonadiabatic Reactive Quenching of OH(A2Σ+) by H2: Origin of High Vibrational Excitation in the H2O Product

Quantum Calculations on a New CCSD(T) Machine-Learned Potential Energy Surface Reveal the Leaky Nature of Gas-Phase Trans and Gauche Ethanol Conformers

The MD17 datasets from the perspective of datasets for gas-phase “small” molecule potentials

Collaborators (6)

Benedetta Mennucci

Joel M. Bowman

Stephen G. Kukolich

Qi Yu

David R. Yarkony

Michele Ceotto

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