Cover Image

"Gold-Standard" $Δ$-Machine Learned and Transferable Potential for Linear Alkanes

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Qu, Chen, Allison, Thomas C., Nandi, Apurba, Houston, Paul L., Yu, Qi, Conte, Riccardo, Bowman, Joel M.
Format:	Preprint
Published:	2025
Subjects:	Chemical Physics
Online Access:	Acceder al recurso
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)

$Δ$-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)

No Headache for PIPs: A PIP Potential for Aspirin Outperforms Other Machine-Learned Potentials
by: Houston, Paul L., 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)

VPT2 Calculations of Vibrational Energies of CH3COOC6H4COOH Done in Seconds on a Laptop Using a Machine Learned Potential
by: Kotaru, Saikiran, et al.
Published: (2026)

The quantum nature of ubiquitous vibrational features revealed for ethylene glycol
by: Nandi, Apurba, et al.
Published: (2025)

Fidelity of Machine Learned Potentials: Quantitative Assessment for Protonated Oxalate
by: Qu, Chen, et al.
Published: (2026)

Can We Learn the Energy of Sublimation of Ice from Water Clusters?
by: Bowman, Joe, 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)

Assessing PIP and sGDML Potential Energy Surfaces for H3O2-
by: Pandey, Priyanka, 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)

Transferability of datasets between Machine-Learning Interaction Potentials
by: Niblett, Samuel P., et al.
Published: (2024)

Quantum mechanical deconstruction of vibrational energy transfer rate and pathways modified by collective vibrational strong coupling
by: Yu, Qi, et al.
Published: (2024)

Transferable Machine Learning Potential X-MACE for Excited States using Integrated DeepSets
by: Barrett, Rhyan, et al.
Published: (2025)

Short-range $Δ$-Machine Learning: A cost-efficient strategy to transfer chemical accuracy to condensed phase systems
by: Mészáros, Bence Balázs, et al.
Published: (2025)

Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
by: Goodwin, Zachary A. H., et al.
Published: (2024)

Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations
by: Eastman, Peter, et al.
Published: (2026)

Does Hessian Data Improve the Performance of Machine Learning Potentials?
by: Rodriguez, Austin, et al.
Published: (2025)

Understanding the Density Maximum of Water with Machine Learned Potentials
by: Song, Yizhi, et al.
Published: (2026)

Machine-Learned Potentials for Solvation Modeling
by: Banchode, Roopshree, et al.
Published: (2025)

Metal‐Free Alkane Functionalization by Organocatalytic and Photoinduced Methods
by: Sha Zhao, et al.
Published: (2025)

Orbital Energies Are Chemical Potentials in Ground-State Density Functional Theory and Excited-State $Δ$SCF Theory
by: Yang, Weitao, et al.
Published: (2024)

Orientational and steric effects in linear alkanoates + N-Alkane mixtures
by: González, Juan Antonio, et al.
Published: (2024)

A Transferable Machine-Learning Model of the Electron Density
by: Grisafi, Andrea, et al.
Published: (2018)

Flexible Uncertainty Calibration for Machine-Learned Interatomic Potentials
by: Ho, Cheuk Hin, et al.
Published: (2025)

The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials
by: Xia, Junfan, et al.
Published: (2025)

Distinguishing Inner and Outer-Sphere Hot Electron Transfer in Au/p-GaN Photocathodes
by: Kiani, Fatemeh, et al.
Published: (2024)

Going for Gold(-Standard): Attaining Coupled Cluster Accuracy in Oxide-Supported Nanoclusters
by: Shi, Benjamin X., et al.
Published: (2023)

Velocity Gauge for Oscillator Strength in $Δ$SCF theory
by: Shen, Yang, et al.
Published: (2026)

Machine Learning-Aided First-Principles Calculations of Redox Potentials
by: Jinnouchi, Ryosuke, et al.
Published: (2023)

MLIPAudit: A benchmarking tool for Machine Learned Interatomic Potentials
by: Wehrhan, Leon, et al.
Published: (2025)

Explicit, Machine-Learned Two-Body Potentials for Molecular Simulations
by: Chaton, Kham Lek, et al.
Published: (2026)

False Metallization in Short-Ranged Machine Learned Interatomic Potentials
by: Parker, Isaac J., et al.
Published: (2026)

Resolving the Body-Order Paradox of Machine Learning Interatomic Potentials
by: Chong, Sanggyu, et al.
Published: (2025)

Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials
by: Chen, Weilong, et al.
Published: (2025)

Potential Distribution Theory of Alchemical Transfer
by: Azimi, Solmaz, et al.
Published: (2024)

Machine Learning Potentials for Heterogeneous Catalysis
by: Omranpour, Amir, et al.
Published: (2024)

Electrochemical Interfaces at Constant Potential: Data-Efficient Transfer Learning for Machine-Learning-Based Molecular Dynamics
by: Bianchi, Michele Giovanni, et al.
Published: (2025)

MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules
by: Kovács, Dávid Péter, et al.
Published: (2023)

Machine Learning Potential for Electrochemical Interfaces with Hybrid Representation of Dielectric Response
by: Zhu, Jia-Xin, et al.
Published: (2024)