CONTE, RICCARDO
CONTE, RICCARDO
Dipartimento di Chimica
The IR spectrum of liquid water in the OH(D)-stretch region and beyond using q-AQUA-pol and the quantum local monomer theory
2025 Q. Yu, J.M. Bowman, C. Qu, A. Nandi, R. Conte, P.L. Houston
Quantumness of classical-trajectory-based methods for vibrational spectroscopy
2025 J. Zeng, R. Conte, M. Ceotto
“Gold-Standard” Δ-Machine Learned Transferable Potential for Linear Alkanes
2025 C. Qu, A. Nandi, P.L. Houston, Q. Yu, R. Conte, J.M. Bowman
A Time Averaged Approach to IR Spectroscopy
2025 C. Lanzi, C. Aieta, M. Ceotto, R. Conte
Extending atomic decomposition and many-body representation with a chemistry-motivated approach to machine learning potentials
2025 Q. Yu, R. Ma, C. Qu, R. Conte, A. Nandi, P. Pandey, P.L. Houston, D.H. Zhang, J.M. Bowman
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
Quantum Nature of Ubiquitous Vibrational Features Revealed for Ethylene Glycol
2025 A. Nandi, R. Conte, P. Pandey, P.L. Houston, C. Qu, Q. Yu, J.M. Bowman
Quantum dynamics through a handful of semiclassical trajectories
2025 C. Aieta, M. Cazzaniga, D. Moscato, C. Lanzi, L. Bocchi, M.M. Costanza, M. Ceotto, R. Conte
Revisiting the H5O2+ IR Spectrum with VSCF/VCI and the Influence of Mark Johnson’s Experiments in Advancing the Theory of Protonated Water Clusters
2025 R. Ma, C. Qu, P.L. Houston, R. Conte, A. Nandi, J.M. Bowman, Q. Yu
An extended semiclassical initial value representation approach to IR spectroscopy
2025 C. Lanzi, C. Aieta, M. Ceotto, R. Conte
Vibrational Spectroscopy Through Time Averaged Fourier Transform of Autocorrelated Molecular Dynamics Data: Introducing the Free SEMISOFT Web‐Platform
2025 R. Conte, M. Gandolfi, D. Moscato, C. Aieta, S. Valtolina, M. Ceotto
A perspective marking 20 years of using permutationally invariant polynomials for molecular potentials
2025 J.M. Bowman, C. Qu, R. Conte, A. Nandi, P.L. Houston, Q. Yu
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
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
Building accurate and efficient ab initio potential energy surfaces for vibrational spectroscopy calculations via permutationally invariant polynomials
2024 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
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
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