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The Journal of Physical Chemistry. A
|
January 5, 2024
A New <i>A Priori</i> Method to Avoid Calculation of Negligible Hamiltonian Matrix Elements in CI Calculation
Paul L Houston, Chen Qu, Qi Yu, et al.
Chemical Science
|
December 5, 2024
Semiclassical description of nuclear quantum effects in solvated and condensed phase molecular systems
Riccardo Conte, Giacomo Mandelli, Giacomo Botti, et al.
The Journal of Physical Chemistry. A
|
July 24, 2025
Revisiting the H<sub>5</sub>O<sub>2</sub><sup>+</sup> IR Spectrum with VSCF/VCI and the Influence of Mark Johnson's Experiments in Advancing the Theory of Protonated Water Clusters
Ruitao Ma, Chen Qu, Paul L Houston, et al.
The Journal of Chemical Physics
|
February 1, 2023
PESPIP: Software to fit complex molecular and many-body potential energy surfaces with permutationally invariant polynomials
Paul L Houston, Chen Qu, Qi Yu, et al.
The Journal of Physical Chemistry. A
|
September 22, 2022
Nonadiabatic Reactive Quenching of OH(<i>A</i><sup>2</sup>Σ<sup>+</sup>) by H<sub>2</sub>: Origin of High Vibrational Excitation in the H<sub>2</sub>O Product
Shanyu Han, Bin Zhao, Riccardo Conte, et al.
The Journal of Physical Chemistry. A
|
April 16, 2024
Assessing Permutationally Invariant Polynomial and Symmetric Gradient Domain Machine Learning Potential Energy Surfaces for H<sub>3</sub>O<sub>2</sub><sup></sup>
Priyanka Pandey, Mrinal Arandhara, Paul L Houston, et al.
Journal of Chemical Theory and Computation
|
October 3, 2024
Δ-Machine Learning to Elevate DFT-Based Potentials and a Force Field to the CCSD(<i>T</i>) Level Illustrated for Ethanol
Apurba Nandi, Priyanka Pandey, Paul L Houston, et al.
Journal of Chemical Theory and Computation
|
February 21, 2024
Formic Acid-Ammonia Heterodimer: A New Δ-Machine Learning CCSD(T)-Level Potential Energy Surface Allows Investigation of the Double Proton Transfer
Paul L Houston, Chen Qu, Qi Yu, et al.
Nature Computational Science
|
April 14, 2025
Extending atomic decomposition and many-body representation with a chemistry-motivated approach to machine learning potentials
Qi Yu, Ruitao Ma, Chen Qu, et al.
The Journal of Physical Chemistry Letters
|
April 16, 2024
Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine
Fuchun Ge, Ran Wang, Chen Qu, et al.
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of 9
Search research articles
Search
Showing results (71-80 of 85) with videos related to
Sort By:
Page
of 9
The Journal of Physical Chemistry. A
|
January 5, 2024
A New <i>A Priori</i> Method to Avoid Calculation of Negligible Hamiltonian Matrix Elements in CI Calculation
Paul L Houston, Chen Qu, Qi Yu, et al.
Chemical Science
|
December 5, 2024
Semiclassical description of nuclear quantum effects in solvated and condensed phase molecular systems
Riccardo Conte, Giacomo Mandelli, Giacomo Botti, et al.
The Journal of Physical Chemistry. A
|
July 24, 2025
Revisiting the H<sub>5</sub>O<sub>2</sub><sup>+</sup> IR Spectrum with VSCF/VCI and the Influence of Mark Johnson's Experiments in Advancing the Theory of Protonated Water Clusters
Ruitao Ma, Chen Qu, Paul L Houston, et al.
The Journal of Chemical Physics
|
February 1, 2023
PESPIP: Software to fit complex molecular and many-body potential energy surfaces with permutationally invariant polynomials
Paul L Houston, Chen Qu, Qi Yu, et al.
The Journal of Physical Chemistry. A
|
September 22, 2022
Nonadiabatic Reactive Quenching of OH(<i>A</i><sup>2</sup>Σ<sup>+</sup>) by H<sub>2</sub>: Origin of High Vibrational Excitation in the H<sub>2</sub>O Product
Shanyu Han, Bin Zhao, Riccardo Conte, et al.
The Journal of Physical Chemistry. A
|
April 16, 2024
Assessing Permutationally Invariant Polynomial and Symmetric Gradient Domain Machine Learning Potential Energy Surfaces for H<sub>3</sub>O<sub>2</sub><sup></sup>
Priyanka Pandey, Mrinal Arandhara, Paul L Houston, et al.
Journal of Chemical Theory and Computation
|
October 3, 2024
Δ-Machine Learning to Elevate DFT-Based Potentials and a Force Field to the CCSD(<i>T</i>) Level Illustrated for Ethanol
Apurba Nandi, Priyanka Pandey, Paul L Houston, et al.
Journal of Chemical Theory and Computation
|
February 21, 2024
Formic Acid-Ammonia Heterodimer: A New Δ-Machine Learning CCSD(T)-Level Potential Energy Surface Allows Investigation of the Double Proton Transfer
Paul L Houston, Chen Qu, Qi Yu, et al.
Nature Computational Science
|
April 14, 2025
Extending atomic decomposition and many-body representation with a chemistry-motivated approach to machine learning potentials
Qi Yu, Ruitao Ma, Chen Qu, et al.
The Journal of Physical Chemistry Letters
|
April 16, 2024
Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine
Fuchun Ge, Ran Wang, Chen Qu, et al.
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of 9