24 citations · 85 across the 11 of their papers we have counts for
11 papers · 1 filter
VPT2 Calculations of Vibrational Energies of CH3COOC6H4COOH Done in Seconds on a Laptop Using a Machine Learned Potential
Saikiran Kotaru, Chen Qu, Apurba Nandi +2
The determination of quartic force fields for use in vibrational second-order perturbation (VPT2) calculations, currently available in numerous electronic structure packages, becom…
Fidelity of Machine Learned Potentials: Quantitative Assessment for Protonated Oxalate
Chen Qu, Paul L. Houston, Qi Yu +5
There has been a veritable explosion of methods and software to perform machine-learned regression on datasets of electronic energies and forces to develop high-dimensional machine…
"Gold-Standard" -Machine Learned and Transferable Potential for Linear Alkanes
Chen Qu, Thomas C. Allison, Apurba Nandi +4
The conformational properties of linear alkanes, CH, have been of intense interest for many years. Experiments and corresponding electronic structure calculations were…
The quantum nature of ubiquitous vibrational features revealed for ethylene glycol
Apurba Nandi, Riccardo Conte, Priyanka Pandey +4
Vibrational properties of molecules are of widespread interest and importance in chemistry and biochemistry. The reliability of widely employed approximate computational methods is…
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)…
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…
A -Machine Learning Approach for Force Fields, Illustrated by a CCSD(T) 4-body Correction to the MB-pol Water Potential
Chen Qu, Qi Yu, Riccardo Conte +3
-Machine Learning (-ML) has been shown to effectively and efficiently bring a low-level ML potential energy surface to CCSD(T) quality. Here we propose extending this approac…