CONTE, RICCARDO
CONTE, RICCARDO
Dipartimento di Chimica
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
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 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
An extended semiclassical initial value representation approach to IR spectroscopy
2025 C. Lanzi, C. Aieta, M. Ceotto, R. Conte
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
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
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
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
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
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
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 Perspective on the Investigation of Spectroscopy and Kinetics of Complex Molecular Systems with Semiclassical Approaches
2024 R. Conte, C. Aieta, M. Cazzaniga, M. Ceotto
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