Image de couverture de livre

Accurate machine learning force fields via experimental and simulation data fusion

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Röcken, Sebastien, Zavadlav, Julija
Format:	Preprint
Publié:	2023
Sujets:	Chemical Physics Machine Learning Computational Physics
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Predicting solvation free energies with an implicit solvent machine learning potential
par: Röcken, Sebastien, et autres
Publié: (2024)

chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics
par: Fuchs, Paul, et autres
Publié: (2024)

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations
par: Fuchs, Paul, et autres
Publié: (2025)

Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration
par: Fuchs, Paul, et autres
Publié: (2025)

Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces
par: Sanocki, Michal, et autres
Publié: (2025)

Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials
par: Chen, Weilong, et autres
Publié: (2025)

Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements
par: Röcken, Sebastien, et autres
Publié: (2025)

Symmetry-invariant quantum machine learning force fields
par: Le, Isabel Nha Minh, et autres
Publié: (2023)

Coarse-Grained Boltzmann Generators
par: Chen, Weilong, et autres
Publié: (2026)

Mapping Still Matters: Coarse-Graining with Machine Learning Potentials
par: Görlich, Franz, et autres
Publié: (2025)

On the design space between molecular mechanics and machine learning force fields
par: Wang, Yuanqing, et autres
Publié: (2024)

Refining Machine Learning Potentials through Thermodynamic Theory of Phase Transitions
par: Fuchs, Paul, et autres
Publié: (2025)

Accurate quantum Monte Carlo forces for machine-learned force fields: Ethanol as a benchmark
par: Slootman, Emiel, et autres
Publié: (2024)

The dark side of the forces: assessing non-conservative force models for atomistic machine learning
par: Bigi, Filippo, et autres
Publié: (2024)

Cartesian atomic cluster expansion for machine learning interatomic potentials
par: Cheng, Bingqing
Publié: (2024)

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials
par: Kim, Dongjin, et autres
Publié: (2025)

Performance of universal machine-learned potentials with explicit long-range interactions in biomolecular simulations
par: Zaverkin, Viktor, et autres
Publié: (2025)

Understanding multi-fidelity training of machine-learned force-fields
par: Gardner, John L. A., et autres
Publié: (2025)

Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning
par: Hu, Frank, et autres
Publié: (2024)

Delta-learned force fields for nonbonded interactions: Addressing the strength mismatch between covalent-nonbonded interaction for global models
par: Cázares-Trejo, Leonardo, et autres
Publié: (2025)

How unconstrained machine-learning models learn physical symmetries
par: Domina, Michelangelo, et autres
Publié: (2026)

Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials
par: Holzenkamp, Matthias, et autres
Publié: (2024)

Using machine learning to map simulated noisy and laser-limited multidimensional spectra to molecular electronic couplings
par: Schultz, Jonathan D., et autres
Publié: (2025)

Latent Ewald summation for machine learning of long-range interactions
par: Cheng, Bingqing
Publié: (2024)

MBD-ML: Many-body dispersion from machine learning for molecules and materials
par: Moerman, Evgeny, et autres
Publié: (2026)

Probing the effects of broken symmetries in machine learning
par: Langer, Marcel F., et autres
Publié: (2024)

A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectors
par: Martinka, Jakub, et autres
Publié: (2025)

Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems
par: Scherbela, Michael, et autres
Publié: (2025)

Long-range electrostatics for machine learning interatomic potentials is easier than we thought
par: Kim, Dongjin, et autres
Publié: (2025)

QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials
par: Zariquiey, Francesc Sabanés, et autres
Publié: (2025)

Knowing when to trust machine-learned interatomic potentials
par: Mehdi, Shams, et autres
Publié: (2026)

Enhancing molecular dynamics with equivariant machine-learned densities
par: Bogojeski, Mihail, et autres
Publié: (2026)

Pushing the limits of unconstrained machine-learned interatomic potentials
par: Bigi, Filippo, et autres
Publié: (2026)

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps
par: Schaaf, Lars L., et autres
Publié: (2024)

Polarizable atomic multipoles for learning long-range electrostatics
par: Kim, Dongjin, et autres
Publié: (2026)

Accurate Computation of Quantum Excited States with Neural Networks
par: Pfau, David, et autres
Publié: (2023)

Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory
par: Schwalbe-Koda, Daniel, et autres
Publié: (2024)

Representing spherical tensors with scalar-based machine-learning models
par: Domina, Michelangelo, et autres
Publié: (2025)

PAL -- Parallel active learning for machine-learned potentials
par: Zhou, Chen, et autres
Publié: (2024)

Extending machine learning model for implicit solvation to free energy calculations
par: Dey, Rishabh, et autres
Publié: (2025)