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The Journal of Chemical Physics|February 2, 2022
Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methodsPaul L Houston, Chen Qu, Apurba Nandi, et al.The Journal of Physical Chemistry Letters|October 18, 2021
A CCSD(T)-Based 4-Body Potential for WaterApurba Nandi, Chen Qu, Paul L Houston, et al.Journal of Chemical Theory and Computation|December 17, 2022
Δ-Machine Learned Potential Energy Surfaces and Force FieldsJoel M Bowman, Chen Qu, Riccardo Conte, et al.Journal of Chemical Theory and Computation|August 11, 2022
Quantum Calculations on a New CCSD(T) Machine-Learned Potential Energy Surface Reveal the Leaky Nature of Gas-Phase <i>Trans</i> and <i>Gauche</i> Ethanol ConformersApurba Nandi, Riccardo Conte, Chen Qu, et al.Journal of Chemical Theory and Computation|May 30, 2023
Interfacing q-AQUA with a Polarizable Force Field: The Best of Both WorldsChen Qu, Qi Yu, Paul L Houston, et al.The Journal of Physical Chemistry Letters|November 20, 2025
"Gold-Standard" Δ-Machine Learned Transferable Potential for Linear AlkanesChen Qu, Apurba Nandi, Paul L Houston, et al.The Journal of Chemical Physics|July 1, 2022
The MD17 datasets from the perspective of datasets for gas-phase "small" molecule potentialsJoel M Bowman, Chen Qu, Riccardo Conte, et al.The Journal of Chemical Physics|May 13, 2025
A perspective marking 20 years of using permutationally invariant polynomials for molecular potentialsJoel M Bowman, Chen Qu, Riccardo Conte, et al.The Journal of Physical Chemistry. A|October 14, 2022
Semiclassical and VSCF/VCI Calculations of the Vibrational Energies of <i>trans</i>- and <i>gauche</i>-Ethanol Using a CCSD(T) Potential Energy SurfaceRiccardo Conte, Apurba Nandi, Chen Qu, et al.The Journal of Chemical Physics|April 7, 2026
Computational spectroscopy using MULTIMODE and machine-learned potentialsChen Qu, Thomas C Allison, Paul L Houston, et al.Pageof 5