CONTE, RICCARDO
CONTE, RICCARDO
Dipartimento di Chimica
Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine
2024 F. Ge, R. Wang, C. Qu, P. Zheng, A. Nandi, R. Conte, P.L. Houston, J.M. Bowman, P.O. Dral
A Perspective on the Investigation of Spectroscopy and Kinetics of Complex Molecular Systems with Semiclassical Approaches
2024 R. Conte, C. Aieta, M. Cazzaniga, M. Ceotto
A time averaged semiclassical approach to IR spectroscopy
2024 C. Lanzi, C. Aieta, M. Ceotto, R. Conte
Assessing Permutationally Invariant Polynomial and Symmetric Gradient Domain Machine Learning Potential Energy Surfaces for H3O2–
2024 P. Pandey, M. Arandhara, P.L. Houston, C. Qu, R. Conte, J.M. Bowman, S.G. Ramesh
Formic Acid–Ammonia Heterodimer: A New Δ-Machine Learning CCSD(T)-Level Potential Energy Surface Allows Investigation of the Double Proton Transfer
2024 P.L. Houston, C. Qu, Q. Yu, P. Pandey, R. Conte, A. Nandi, J.M. Bowman, S.G. Kukolich
A New A Priori Method to Avoid Calculation of Negligible Hamiltonian Matrix Elements in CI Calculation
2024 P.L. Houston, C. Qu, Q. Yu, P. Pandey, R. Conte, A. Nandi, J.M. Bowman
Ab Initio Potential Energy Surface for NaCl–H2 with Correct Long-Range Behavior
2024 P. Pandey, C. Qu, A. Nandi, Q. Yu, P.L. Houston, R. Conte, J.M. Bowman
No Headache for PIPs: A PIP Potential for Aspirin Runs Much Faster and with Similar Precision Than Other Machine-Learned Potentials
2024 P.L. Houston, C. Qu, Q. Yu, P. Pandey, R. Conte, A. Nandi, J.M. Bowman
Δ-Machine Learning to Elevate DFT-Based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol
2024 A. Nandi, P. Pandey, P.L. Houston, C. Qu, Q. Yu, R. Conte, A. Tkatchenko, J.M. Bowman
Unraveling Water Solvation Effects with Quantum Mechanics/Molecular Mechanics Semiclassical Vibrational Spectroscopy: The Case of Thymidine
2024 D. Moscato, G. Mandelli, M. Bondanza, F. Lipparini, R. Conte, B. Mennucci, M. Ceotto
Semiclassical vibrational spectroscopy from small molecules to solvated biomolecules
2023 R. Conte, C. Lanzi, G. Botti, G. Mandelli, D. Moscato, C. Aieta, M. Ceotto
A semiclassical route to the calculation of IR spectra
2023 C. Lanzi, C. Aieta, M. Ceotto, R. Conte
Δ-Machine Learned Potential Energy Surfaces and Force Fields
2023 J.M. Bowman, C. Qu, R. Conte, A. Nandi, P.L. Houston, Q. Yu
The first HyDRA challenge for computational vibrational spectroscopy
2023 T.L. Fischer, M. Bödecker, S.M. Schweer, J. Dupont, V. Lepère, A. Zehnacker-Rentien, M.A. Suhm, B. Schröder, T. Henkes, D.M. Andrada, R.M. Balabin, H.K. Singh, H.P. Bhattacharyya, M. Sarma, S. Käser, K. Töpfer, L.I. Vazquez-Salazar, E.D. Boittier, M. Meuwly, G. Mandelli, C. Lanzi, R. Conte, M. Ceotto, F. Dietrich, V. Cisternas, R. Gnanasekaran, M. Hippler, M. Jarraya, M. Hochlaf, N. Viswanathan, T. Nevolianis, G. Rath, W.A. Kopp, K. Leonhard, R.A. Mata
Diffusion Monte Carlo and PIMD calculations of radial distribution functions using an updated CCSD(T) potential for CH5+
2023 C. Qu, Q. Yu, P.L. Houston, P. Pandey, R. Conte, A. Nandi, J.M. Bowman
Ring-Polymer Instanton Tunneling Splittings of Tropolone and Isotopomers using a Δ-Machine Learned CCSD(T) Potential: Theory and Experiment Shake Hands
2023 A. Nandi, G. Laude, S.S. Khire, N.D. Gurav, C. Qu, R. Conte, Q. Yu, S. Li, P.L. Houston, S.R. Gadre, J.O. Richardson, F.A. Evangelista, J.M. Bowman
A Status Report on "Gold Standard" Machine-Learned Potentials for Water
2023 Q. Yu, C. Qu, P.L. Houston, A. Nandi, P. Pandey, R. Conte, J.M. Bowman
PESPIP: Software to Fit Complex Molecular and Many-body Potential Energy Surfaces with Permutationally Invariant Polynomials
2023 P.L. Houston, C. Qu, Q. Yu, R. Conte, A. Nandi, J.K. Li, J.M. Bowman
Using AS SCIVR to understand Proline vibrational spectrum
2023 G. Botti, C.D. Aieta, M. Ceotto, R. Conte
From anharmonicity to Nuclear Quantum Effects in medium and large sized molecular systems
2023 D. Moscato, R. Conte, C. Aieta, G. Botti, M. Cazzaniga, M. Gandolfi, C. Lanzi, G. Mandelli, M. Ceotto