CONTE, RICCARDO
CONTE, RICCARDO
Dipartimento di Chimica
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
A Time Averaged 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
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
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
Building accurate and efficient ab initio potential energy surfaces for vibrational spectroscopy calculations via permutationally invariant polynomials
2024 R. Conte
A time averaged semiclassical approach to IR spectroscopy
2024 C. Lanzi, C. Aieta, M. Ceotto, 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
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
A Time Averaged Semiclassical Approach to IR Spectroscopy
2024 C. Lanzi, C. Aieta, M. Ceotto, R. Conte
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
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
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
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
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
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
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
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
Using AS SCIVR to understand Proline vibrational spectrum
2023 G. Botti, C.D. Aieta, M. Ceotto, R. Conte
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