CONTE, RICCARDO
CONTE, RICCARDO
Dipartimento di Chimica
Semiclassical description of nuclear quantum effects in solvated and condensed phase molecular systems
2025 R. Conte, G. Mandelli, G. Botti, D. Moscato, C. Lanzi, M. Cazzaniga, C. Aieta, M. Ceotto
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
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
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
A time averaged semiclassical approach to IR spectroscopy
2024 C. Lanzi, C. Aieta, M. Ceotto, R. Conte
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
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
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
Dynamics Calculations of the Flexibility and Vibrational Spectrum of the Linear Alkane C14H30, Based on Machine-Learned Potentials [Dynamics Calculations of the Flexibility and Vibrational Spectrum of the Linear Alkane C₁₄H₃₀, Based on Machine-Learned Potentials]
2024 C. Qu, P.L. Houston, R. Conte, J.M. Bowman
Building accurate and efficient ab initio potential energy surfaces for vibrational spectroscopy calculations via permutationally invariant polynomials
2024 R. Conte
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
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
Anharmonic Assignment of the Water Octamer Spectrum in the OH Stretch Region
2023 D. Barbiero, G. Bertaina, M. Ceotto, R. Conte
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
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
Anharmonicity and quantum nuclear effects in theoretical vibrational spectroscopy: A molecular tale of two cities
2023 R. Conte, C. Aieta, G. Botti, M. Cazzaniga, M. Gandolfi, C. Lanzi, G. Mandelli, D. Moscato, M. Ceotto
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
Investigating the Spectroscopy of the Gas Phase Guanine-Cytosine Pair: Keto versus Enol Configurations
2023 G. Botti, M. Ceotto, R. Conte
Machine learning classification can significantly reduce the cost of calculating the Hamiltonian matrix in CI calculations
2023 C. Qu, P.L. Houston, Q. Yu, R. Conte, P. Pandey, A. Nandi, J.M. Bowman