24 citations · 87 across the 13 of their papers we have counts for
8 papers · 1 filter
Extending the atomic decomposition and many-body representation, a chemistry-motivated monomer-centered approach for machine learning potentials
Qi Yu, Ruitao Ma, Chen Qu +6
Most widely used machine learned (ML) potentials for condensed phase applications rely on many-body permutationally invariant polynomial (PIP) or atom-centered neural networks (NN)…
Quantum mechanical deconstruction of vibrational energy transfer rate and pathways modified by collective vibrational strong coupling
Qi Yu, Dong H. Zhang, Joel M. Bowman
Recent experiments have demonstrated that vibrational strong coupling (VSC) between molecular vibrations and the optical cavity field can modify vibrational energy transfer (VET) p…
Inelastic Triatom-Atom Quantum Close-Coupling Dynamics in Full Dimensionality: all rovibrational mode quenching of water due to H impact on a six-dimensional potential energy surface
Benhui Yang, Chen Qu, J. M. Bowman +5
The rovibrational level populations, and subsequent emission in various astrophysical environments, is driven by inelastic collision processes. The available rovibrational rate coe…
Can We Learn the Energy of Sublimation of Ice from Water Clusters?
Joe Bowman, Qi Yu, Chen Qu +2
This short paper reports a study of the electronic dissociation energies, De, of water clusters from direct ab initio (mostly CCSD(T)) calculations and the q-AQUA and MB-pol potent…
-Machine Learning to Elevate DFT-based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol
Apurba Nandi, Priyanka Pandey, Paul L. Houston +5
Progress in machine learning has facilitated the development of potentials that offer both the accuracy of first-principles techniques and vast increases in the speed of evaluation…
Tell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine
Fuchun Ge, Ran Wang, Chen Qu +6
Machine learning potentials (MLPs) are widely applied as an efficient alternative way to represent potential energy surfaces (PES) in many chemical simulations. The MLPs are often…
Assessing PIP and sGDML Potential Energy Surfaces for H3O2-
Priyanka Pandey, Mrinal Arandhara, Paul L. Houston +4
Here we assess two machine-learned potentials, one using the symmetric gradient domain machine learning (sGDML) method and one based on permutationally invariant polynomials (PIPs)…
No Headache for PIPs: A PIP Potential for Aspirin Outperforms Other Machine-Learned Potentials
Paul L. Houston, Chen Qu, Qi Yu +4
Assessments of machine-learned (ML) potentials are an important aspect of the rapid development of this field. We recently reported an assessment of the linear-regression permutati…