VPT2 Calculations of Vibrational Energies of CH3COOC6H4COOH Done in Seconds on a Laptop Using a Machine Learned Potential
Fuente:
arXiv
Saved in:
| Main Authors: | Kotaru, Saikiran, Qu, Chen, Nandi, Apurba, Houston, Paul L., Bowman, Joel M. |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A $Δ$-Machine Learning Approach for Force Fields, Illustrated by a CCSD(T) 4-body Correction to the MB-pol Water Potential
by: Qu, Chen, et al.
Published: (2022)
by: Qu, Chen, et al.
Published: (2022)
No Headache for PIPs: A PIP Potential for Aspirin Outperforms Other Machine-Learned Potentials
by: Houston, Paul L., et al.
Published: (2024)
by: Houston, Paul L., et al.
Published: (2024)
"Gold-Standard" $Δ$-Machine Learned and Transferable Potential for Linear Alkanes
by: Qu, Chen, et al.
Published: (2025)
by: Qu, Chen, et al.
Published: (2025)
Fidelity of Machine Learned Potentials: Quantitative Assessment for Protonated Oxalate
by: Qu, Chen, et al.
Published: (2026)
by: Qu, Chen, et al.
Published: (2026)
$Δ$-Machine Learning to Elevate DFT-based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol
by: Nandi, Apurba, et al.
Published: (2024)
by: Nandi, Apurba, et al.
Published: (2024)
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
by: Houston, Paul L., et al.
Published: (2021)
by: Houston, Paul L., et al.
Published: (2021)
The quantum nature of ubiquitous vibrational features revealed for ethylene glycol
by: Nandi, Apurba, et al.
Published: (2025)
by: Nandi, Apurba, et al.
Published: (2025)
Assessing PIP and sGDML Potential Energy Surfaces for H3O2-
by: Pandey, Priyanka, et al.
Published: (2024)
by: Pandey, Priyanka, et al.
Published: (2024)
Tell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine
by: Ge, Fuchun, et al.
Published: (2024)
by: Ge, Fuchun, et al.
Published: (2024)
Can We Learn the Energy of Sublimation of Ice from Water Clusters?
by: Bowman, Joe, et al.
Published: (2024)
by: Bowman, Joe, et al.
Published: (2024)
Extending the atomic decomposition and many-body representation, a chemistry-motivated monomer-centered approach for machine learning potentials
by: Yu, Qi, et al.
Published: (2024)
by: Yu, Qi, et al.
Published: (2024)
Machine-Learned Electrostatic Potentials for Accurate Hydration Free Energy Calculations
by: Hilfiker, Mathias, et al.
Published: (2025)
by: Hilfiker, Mathias, et al.
Published: (2025)
Machine Learning-Aided First-Principles Calculations of Redox Potentials
by: Jinnouchi, Ryosuke, et al.
Published: (2023)
by: Jinnouchi, Ryosuke, et al.
Published: (2023)
Anharmonic Vibrational States of Double-Well Potentials in the Solid State from DFT Calculations
by: Mitoli, Davide, et al.
Published: (2024)
by: Mitoli, Davide, et al.
Published: (2024)
Modal Backflow Neural Quantum States for Anharmonic Vibrational Calculations
by: Ding, Lexin, et al.
Published: (2025)
by: Ding, Lexin, et al.
Published: (2025)
Reactive Chemistry at Unrestricted Coupled Cluster Level: High-throughput Calculations for Training Machine Learning Potentials
by: Allen, Alice E. A., et al.
Published: (2025)
by: Allen, Alice E. A., et al.
Published: (2025)
Tree Tensor Networks Methods for Efficient Calculation of Molecular Vibrational Spectra
by: Sun, Shuo, et al.
Published: (2025)
by: Sun, Shuo, et al.
Published: (2025)
Quasi-classical Trajectory Calculations on a Two-state Potential Energy Surface Including Nonadiabatic Coupling Terms as Friction for D+ + H2 Collisions
by: Mukherjee, Soumya, et al.
Published: (2024)
by: Mukherjee, Soumya, et al.
Published: (2024)
Machine Learned Potential for High-Throughput Phonon Calculations of Metal-Organic Frameworks
by: Elena, Alin Marin, et al.
Published: (2024)
by: Elena, Alin Marin, et al.
Published: (2024)
Analytic Computation of Vibrational Circular Dichroism Spectra Using Second-Order Møller-Plesset Perturbation Theory
by: Shumberger, Brendan M., et al.
Published: (2025)
by: Shumberger, Brendan M., et al.
Published: (2025)
Theoretical Kinetics Study of the OH + CH3SH Reaction Based on an Analytical Full‐Dimensional Potential Energy Surface
by: Joaquin Espinosa‐Garcia, et al.
Published: (2025)
by: Joaquin Espinosa‐Garcia, et al.
Published: (2025)
QMCTorch: Molecular Wavefunctions with Neural Components for Energy and Force Calculations
by: Renaud, Nicolas
Published: (2025)
by: Renaud, Nicolas
Published: (2025)
OH-Formation Following Vibrationally Induced Reaction Dynamics of H$_2$COO
by: Song, Kaisheng, et al.
Published: (2024)
by: Song, Kaisheng, et al.
Published: (2024)
Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces
by: Vazquez-Salazar, Luis Itza, et al.
Published: (2024)
by: Vazquez-Salazar, Luis Itza, et al.
Published: (2024)
Fault-tolerant Quantum Chemical Calculations with Improved Machine-Learning Models
by: Yuan, Kai, et al.
Published: (2024)
by: Yuan, Kai, et al.
Published: (2024)
From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
by: Liu, Ryan, et al.
Published: (2026)
by: Liu, Ryan, et al.
Published: (2026)
Quantum-Centric Alchemical Free Energy Calculations
by: Bazayeva, Milana, et al.
Published: (2025)
by: Bazayeva, Milana, et al.
Published: (2025)
Recovering Exact Vibrational Energies Within a Phase Space Electronic Structure Framework
by: Wu, Xinchun, et al.
Published: (2025)
by: Wu, Xinchun, et al.
Published: (2025)
Linear Scaling Calculation of Atomic Forces and Energies with Machine Learning Local Density Matrix
by: Xin, Zaizhou, et al.
Published: (2025)
by: Xin, Zaizhou, et al.
Published: (2025)
Deep Potential-Driven Molecular Dynamics of CO Ice Analogues: Investigating Desorption Following Vibrational Excitation
by: Infuso, Maxime, et al.
Published: (2025)
by: Infuso, Maxime, et al.
Published: (2025)
Locating Ab Initio Transition States via Approximate Geodesics on Machine Learned Potential Energy Surfaces
by: Hait, Diptarka, et al.
Published: (2025)
by: Hait, Diptarka, et al.
Published: (2025)
Musical Molecules: Sonifying the IR Spectra and Modeling Intramolecular Vibrational Energy Redistribution of Small Molecules
by: Kim, Sophia H., et al.
Published: (2026)
by: Kim, Sophia H., et al.
Published: (2026)
Vibrational Quantum-State-Controlled Reactivity in the O2+ + C3H4 Reaction
by: Zagorec-Marks, C., et al.
Published: (2026)
by: Zagorec-Marks, C., et al.
Published: (2026)
Design, Assessment, and Application of Machine Learning Potential Energy Surfaces
by: Andreichev, Valerii, et al.
Published: (2025)
by: Andreichev, Valerii, et al.
Published: (2025)
Exchange-Correlation Potentials and Energy Densities through Orbital Averaging and Aufbau Integration
by: Khanna, Vaibhav, et al.
Published: (2025)
by: Khanna, Vaibhav, et al.
Published: (2025)
Rectification of Vibrational Energy Transfer in Driven Chiral Molecules
by: Feng, Jichen, et al.
Published: (2025)
by: Feng, Jichen, et al.
Published: (2025)
QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials
by: Zariquiey, Francesc Sabanés, et al.
Published: (2025)
by: Zariquiey, Francesc Sabanés, et al.
Published: (2025)
Binding Selectivity Analysis from Alchemical Receptor Hopping and Swapping Free Energy Calculations
by: Azimi, Solmaz, et al.
Published: (2024)
by: Azimi, Solmaz, et al.
Published: (2024)
Automated Workflow for Absolute Binding Free Energy Calculations with Implicit Solvent and Double Decoupling
by: Ayoub, Steven, et al.
Published: (2025)
by: Ayoub, Steven, et al.
Published: (2025)
Simulating the Dehydration Process by Adsorption and Diffusion of H2O and CH4 on the Silica
by: Mohammad Teimouri, et al.
Published: (2025)
by: Mohammad Teimouri, et al.
Published: (2025)
Similar Items
-
A $Δ$-Machine Learning Approach for Force Fields, Illustrated by a CCSD(T) 4-body Correction to the MB-pol Water Potential
by: Qu, Chen, et al.
Published: (2022) -
No Headache for PIPs: A PIP Potential for Aspirin Outperforms Other Machine-Learned Potentials
by: Houston, Paul L., et al.
Published: (2024) -
"Gold-Standard" $Δ$-Machine Learned and Transferable Potential for Linear Alkanes
by: Qu, Chen, et al.
Published: (2025) -
Fidelity of Machine Learned Potentials: Quantitative Assessment for Protonated Oxalate
by: Qu, Chen, et al.
Published: (2026) -
$Δ$-Machine Learning to Elevate DFT-based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol
by: Nandi, Apurba, et al.
Published: (2024)