160 citations · 631 across the 18 of their papers we have counts for
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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)…
-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…
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 Rate Constants III: Application to the Cl(P) + CH CH + HCl Reaction
Paul L. Houston, Apurba Nandi, Joel M. Bowman
The temperature dependence of the thermal rate constant for the reaction Cl(P) + CH CH + HCl is calculated using a Gaussian Process machine learning (ML)…
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…
Quantum calculations on a new CCSD(T) machine-learned PES reveal the leaky nature of gas-phase and ethanol conformers
Apurba Nandi, Riccardo Conte, Chen Qu +3
Ethanol is a molecule of fundamental interest in combustion, astrochemistry, and condensed phase as a solvent. It is characterized by two methyl rotors and () and $ga…
The MD17 Datasets from the Perspective of Datasets for Gas-Phase "Small" Molecule Potentials
Joel M. Bowman, Chen Qu Riccardo Conte, Apurba Nandi +2
There has been great progress in developing methods for machine-learned potential energy surfaces. There have also been important assessments of these methods by comparing so-calle…
q-AQUA: a many-body CCSD(T) water potential, including 4-body interactions, demonstrates the quantum nature of water from clusters to the liquid phase
Qi Yu, Chen Qu, Paul L. Houston +3
Many model potential energy surfaces (PESs) have been reported for water; however, none are strictly from "first principles". Here we report such a potential, based on a many-body…
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 methods
Paul L. Houston, Chen Qu, Apurba Nandi +3
Permutationally invariant polynomial (PIP) regression has been used to obtain machine-learned (ML) potential energy surfaces, including analytical gradients, for many molecules and…
Enabling rapid and accurate construction of CCSD(T)-level potential energy surface of large molecules using molecular tailoring approach
Subodh S. Khire, Nalini D. Gurav, Apurba Nandi +1
The construction of the potential energy surface (PES) of even a medium-sized molecule employing correlated theory, such as CCSD(T), is an arduous task due to the high computationa…
A CCSD(T)-based permutationally invariant polynomial 4-body potential for water
Apurba Nandi, Chen Qu, Paul L. Houston +2
We report a permutationally invariant polynomial (PIP) potential energy surface for the water 4-body interaction. This 12-atom PES is a fit to 2119, symmetry-unique, CCSD(T)-F12a/h…
Breaking the Coupled Cluster Barrier for Machine Learned Potentials of Large Molecules: The Case of 15-atom Acetylacetone
Chen Qu, Paul Houston, Riccardo Conte +2
Machine-learned potential energy surfaces (PESs) for molecules with more than 10 atoms are typically forced to use lower-level electronic structure methods such as density function…
-Machine Learning for Potential Energy Surfaces: A PIP approach to bring a DFT-based PES to CCSD(T) Level of Theory
Apurba Nandi, Chen Qu, Paul Houston +2
``-machine learning" refers to a machine learning approach to bring a property such as a potential energy surface (PES) based on low-level (LL) density functional theory (DFT) e…