Cover Image

Short-range $Δ$-Machine Learning: A cost-efficient strategy to transfer chemical accuracy to condensed phase systems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Mészáros, Bence Balázs, Szabó, András, Daru, János
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

Systematic incorporation of nuclear quantum effects into atomistic simulations by smoothed trajectory analysis
by: Madarász, Ádám, et al.
Published: (2026)

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

Nuclear Quantum Effects in Liquid Water Are Negligible for Structure but Significant for Dynamics
by: Stolte, Nore, et al.
Published: (2024)

Adversarial reverse mapping of condensed-phase molecular structures: Chemical transferability
by: Stieffenhofer, Marc, et al.
Published: (2021)

Towards chemical accuracy for chemi- and physisorption with an efficient density functional
by: Kothakonda, Manish, et al.
Published: (2024)

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

"Gold-Standard" $Δ$-Machine Learned and Transferable Potential for Linear Alkanes
by: Qu, Chen, et al.
Published: (2025)

Including photoexcitation explicitly in trajectory-based nonadiabatic dynamics at no cost
by: Janoš, Jiří, et al.
Published: (2024)

Attaining high accuracy for charge-transfer excitations in non-covalent complexes at second-order perturbation cost: the importance of state-specific self-consistency
by: Tran, Nhan Tri, et al.
Published: (2024)

Hammett-Inspired Product Baseline for Data-efficient $Δ$-ML in Chemical Space
by: Rakotonirina, V. Diana, et al.
Published: (2023)

Machine learning the two-electron reduced density matrix in molecules and condensed phases
by: B., Jessica A. Martinez, et al.
Published: (2026)

Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo
by: Szabó, P. Bernát, 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)

Description of the condensed phases of water in terms of Bose-Einstein condensates
by: Fillaux, François
Published: (2023)

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)

Targeted energy transfer dynamics and chemical reactions
by: Almazova, N., et al.
Published: (2024)

Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
by: Brunken, Christoph, et al.
Published: (2025)

Ab initio surface chemistry with chemical accuracy
by: Ye, Hong-Zhou, et al.
Published: (2023)

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

Combustion Toolbox: An open-source thermochemical code for gas- and condensed-phase problems involving chemical equilibrium
by: Cuadra, Alberto, et al.
Published: (2024)

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

Machine learning for accuracy in density functional approximations
by: Voss, Johannes
Published: (2023)

Time-dependent electron transfer and energy dissipation in condensed media
by: Arguelles, Elvis F., et al.
Published: (2023)

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

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

Photodesorption efficiency of OH radical on the ice surface in the wavelength range from ultraviolet to visible
by: Sie, Ni-En, et al.
Published: (2024)

Two-dimensional electronic spectroscopy in the condensed phase using equivariant transformer accelerated molecular dynamics simulations
by: Kelly, Joseph, et al.
Published: (2025)

Ab-initio simulation of excited-state potential energy surfaces with transferable deep quantum Monte Carlo
by: Schätzle, Zeno, et al.
Published: (2025)

Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials
by: Matin, Sakib, et al.
Published: (2025)

Combining Hammett $σ$ constants for $Δ$-machine learning and catalyst discovery
by: Rakotonirina, V. Diana, et al.
Published: (2024)

Regularized relativistic corrections for polyelectronic and polyatomic systems with explicitly correlated Gaussians
by: Rácsai, Balázs, et al.
Published: (2024)

Efficient first-principles modeling of complex molecular crystals at sub-chemical accuracy
by: Shi, Benjamin X., et al.
Published: (2026)

Thawed Gaussian wavepacket dynamics with $Δ$-machine learned potentials
by: Gherib, Rami, et al.
Published: (2024)

Blackbody radiation and thermal effects on chemical reactions and phase transitions in cavities
by: Pannir-Sivajothi, Sindhana, et al.
Published: (2024)

Machine Learning for Polaritonic Chemistry: Accessing chemical kinetics
by: Schäfer, Christian, et al.
Published: (2023)

Classical and quantum light-induced non-adiabaticity in molecular systems
by: Fábri, Csaba, et al.
Published: (2024)

An improved guess for the variational calculation of charge-transfer excitations in large systems
by: Bogo, Nicola, et al.
Published: (2025)

Classical solution of the FeMo-cofactor model to chemical accuracy and its implications
by: Zhai, Huanchen, et al.
Published: (2026)

A large language model system for the field of chemical engineering technology
by: Zhang, Heng, et al.
Published: (2025)

An efficient method based on the evolutionary center algorithm for optimizing chemical-diffusive models for flame acceleration and DDT
by: Xiao, Huahua, et al.
Published: (2026)