Cover Image

Benchmarking foundation potentials against quantum chemistry methods for predicting molecular redox potentials

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Yicheng, Cheng, Lixue, Jing, Yan, Zhong, Peichen
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

Machine learning potentials for redox chemistry in solution
by: Kocer, Emir, et al.
Published: (2024)

Machine learning interatomic potential can infer electrical response
by: Zhong, Peichen, et al.
Published: (2025)

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials
by: Kim, Dongjin, et al.
Published: (2025)

Non-empirical prediction of the length-dependent ionization potential in molecular chains
by: Ohad, Guy, et al.
Published: (2024)

Absolute standard hydrogen electrode potential and redox potentials of atoms and molecules: machine learning aided first principles calculations
by: Jinnouchi, Ryosuke, et al.
Published: (2024)

Towards chemical accuracy with shallow quantum circuits: A Clifford-based Hamiltonian engineering approach
by: Sun, Jiace, et al.
Published: (2023)

Constructing accurate and efficient general-purpose atomistic machine learning model with transferable accuracy for quantum chemistry
by: Chen, Yicheng, et al.
Published: (2024)

Electrochemical CO2 capture with pH-independent redox chemistry
by: Kim, Sang Cheol, et al.
Published: (2025)

Tabular foundation models for in-context prediction of molecular properties
by: Hicham, Karim K. Ben, et al.
Published: (2026)

Benchmarking machine-learned interatomic potentials for molecular infrared spectroscopy
by: Bhatia, Nitik, et al.
Published: (2026)

A foundation machine learning potential with polarizable long-range interactions for materials modelling
by: Gao, Rongzhi, et al.
Published: (2024)

Engineering molecular potential energy surfaces using magnetic cavity quantum electrodynamics
by: Weber, Lukas, et al.
Published: (2026)

Self-interaction corrected Kohn-Sham effective potentials using the density-consistent effective potential method
by: Diaz, Carlos M., et al.
Published: (2021)

High temperature melting of dense molecular hydrogen from machine-learning interatomic potentials trained on quantum Monte Carlo
by: Goswami, Shubhang, et al.
Published: (2024)

Universal neural network potentials as descriptors: Towards scalable chemical property prediction using quantum and classical computers
by: Shiota, Tomoya, et al.
Published: (2024)

Effective classical potential for quantum statistical averages
by: Sadhasivam, Vijay Ganesh, et al.
Published: (2026)

How accurate are foundational machine learning interatomic potentials for heterogeneous catalysis?
by: Kempen, Luuk H. E., et al.
Published: (2025)

Autonomous Quantum Simulation through Large Language Model Agents
by: Li, Weitang, et al.
Published: (2026)

A foundation model for atomistic materials chemistry
by: Batatia, Ilyes, et al.
Published: (2023)

Atomistic Modeling of Chemical Disorder in Materials: Bridging Classical Methods and AI-Assisted Approaches
by: Peng, Jiayu, et al.
Published: (2026)

Crystal structure prediction with host-guided inpainting generation and foundation potentials
by: Zhong, Peichen, et al.
Published: (2025)

Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
by: Stark, Wojciech G., et al.
Published: (2024)

MACE4IRmol: An uncertainty-aware foundation model for molecular infrared spectroscopy
by: Bhatia, Nitik, et al.
Published: (2025)

Benchmarking short-range machine learning potentials for atomistic simulations of metal/electrolyte interfaces
by: de Kam, Lucas B. T., et al.
Published: (2026)

Multi-head committees enable direct uncertainty prediction for atomistic foundation models
by: Beck, Hubert, et al.
Published: (2025)

An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking
by: Foster, Adam, et al.
Published: (2025)

Spin-coupled molecular orbitals: chemical intuition meets quantum chemistry
by: Marti-Dafcik, Daniel, et al.
Published: (2024)

Accurate nuclear quantum statistics on machine-learned classical effective potentials
by: Zaporozhets, Iryna, et al.
Published: (2024)

Multitask methods for predicting molecular properties from heterogeneous data
by: Fisher, Katharine, et al.
Published: (2024)

Development of machine-learned interatomic potentials to predict structure, transport, and reactivity in platinum-based fuel cells
by: Fazel, Kamron, et al.
Published: (2025)

Subspace methods for the simulation of molecular response properties on a quantum computer
by: Reinholdt, Peter, et al.
Published: (2024)

Exchange-correlation potential built on the derivative discontinuity of electron density
by: Huang, Chen
Published: (2024)

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction
by: Bhatia, Nitik, et al.
Published: (2025)

First principles prediction of wavelength-dependent isomerization quantum yields of a second-generation molecular nanomotor
by: Lucia-Tamudo, Jesús, et al.
Published: (2025)

Random sampling versus active learning algorithms for machine learning potentials of quantum liquid water
by: Stolte, Nore, et al.
Published: (2024)

Multistate Coupled Diabatic Neural Network potential for the quantum non-adiabatic Photofragmentation of CH$_2^+$
by: del Mazo-Sevillano, Pablo, et al.
Published: (2026)

All-in-one foundational models learning across quantum chemical levels
by: Chen, Yuxinxin, et al.
Published: (2024)

A constant potential reactor framework for electrochemical reaction simulations
by: Chen, Letian, et al.
Published: (2024)

Molecular electrostatic potentials from machine learning models for dipole and quadrupole predictions
by: Muuga, Kadri, et al.
Published: (2026)

Teachers that teach the irrelevant: Pre-training machine learned interaction potentials with classical force fields for robust molecular dynamics simulations
by: Yuan, Eric C. -Y., et al.
Published: (2025)