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6 papers · 1 filter
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…
AGP-based unitary coupled cluster theory for quantum computers
Armin Khamoshi, Guo P. Chen, Francesco A. Evangelista +1
Electronic structure methods typically benefit from symmetry breaking and restoration, specially in the strong correlation regime. The same goes for Ansätze on a quantum computer.…
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…