activity
20072022
most cited-Machine Learning for Potential Energy Surfaces: A PIP approach to bring a DFT-based PES to CCSD(T) Level of Theory

160 citations · 803 across the 14 of their papers we have counts for

collaborators

14 papers

physics.chem-ph2022★ 9 cited

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)…

physics.chem-ph2022★ 16 cited

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…

physics.chem-ph2022★ 35 cited

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…

physics.chem-ph2022★ 125 cited

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…

physics.chem-ph2021★ 53 cited

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…

physics.chem-ph2021★ 42 cited

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…