Riccardo Conte
Affiliations: | Università degli Studi di Milano, Italy, Milano, Lombardia, Italy |
Area:
Theoretical Chemistry, Semiclassical Dynamics, QCT DynamicsGoogle:
"Riccardo Conte"Mean distance: (not calculated yet)
Parents
Sign in to add mentorJoel M. Bowman | post-doc | Emory | |
Michele Ceotto | post-doc | Università degli Studi di Milano | |
Eli Pollak | post-doc | Weizmann Institute |
BETA: Related publications
See more...
Publications
You can help our author matching system! If you notice any publications incorrectly attributed to this author, please sign in and mark matches as correct or incorrect. |
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 |