Riccardo Conte

Affiliations: 
Università degli Studi di Milano, Italy, Milano, Lombardia, Italy 
Area:
Theoretical Chemistry, Semiclassical Dynamics, QCT Dynamics
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"Riccardo Conte"
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Publications

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Conte R, Mandelli G, Botti G, et al. (2024) Semiclassical description of nuclear quantum effects in solvated and condensed phase molecular systems. Chemical Science. 16: 20-28
Qu C, Houston PL, Conte R, et al. (2024) Dynamics Calculations of the Flexibility and Vibrational Spectrum of the Linear Alkane CH, Based on Machine-Learned Potentials. The Journal of Physical Chemistry. A. 128: 10713-10722
Nandi A, Pandey P, Houston PL, et al. (2024) Δ-Machine Learning to Elevate DFT-Based Potentials and a Force Field to the CCSD() Level Illustrated for Ethanol. Journal of Chemical Theory and Computation
Conte R, Aieta C, Cazzaniga M, et al. (2024) A Perspective on the Investigation of Spectroscopy and Kinetics of Complex Molecular Systems with Semiclassical Approaches. The Journal of Physical Chemistry Letters. 7566-7576
Lanzi C, Aieta C, Ceotto M, et al. (2024) A time averaged semiclassical approach to IR spectroscopy. The Journal of Chemical Physics. 160
Ge F, Wang R, Qu C, et al. (2024) Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine. The Journal of Physical Chemistry Letters. 4451-4460
Pandey P, Arandhara M, Houston PL, et al. (2024) Assessing Permutationally Invariant Polynomial and Symmetric Gradient Domain Machine Learning Potential Energy Surfaces for HO. The Journal of Physical Chemistry. A
Houston PL, Qu C, Yu Q, et al. (2024) No Headache for PIPs: A PIP Potential for Aspirin Runs Much Faster and with Similar Precision Than Other Machine-Learned Potentials. Journal of Chemical Theory and Computation
Moscato D, Mandelli G, Bondanza M, et al. (2024) Unraveling Water Solvation Effects with Quantum Mechanics/Molecular Mechanics Semiclassical Vibrational Spectroscopy: The Case of Thymidine. Journal of the American Chemical Society
Houston PL, Qu C, Yu Q, et al. (2024) Formic Acid-Ammonia Heterodimer: A New Δ-Machine Learning CCSD(T)-Level Potential Energy Surface Allows Investigation of the Double Proton Transfer. Journal of Chemical Theory and Computation
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