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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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6 results for “force-matching”

Operator Forces For Coarse-Grained Molecular Dynamicsphysics.chem-ph

New method uses normalizing flows to improve force fields for coarse-grained molecular dynamics.

problem Lack of reference atomistic forces makes force matching infeasible for MLCG force fields.

method Introduces noise-based kernels adapted to low-data regimes using normalizing flows.

result Flow-based kernels reduce local distortions while preserving global accuracy.

coarse-grained molecular dynamics machine learning force fields normalizing flows noise-based kernels

2025-06-24abs ↗pdf ↗

Smooth Normalizing Flowsstat.ML

Smooth flows for physical systems with smooth energies and forces.

problem Smooth energies for physical simulations and force computation.

method Smooth mixture transformations on compact intervals and hypertori, using root-finding and the inverse function theorem.

result Smooth flows allow training by force matching and use as molecular dynamics potentials.

normalizing flows smooth energies force matching molecular dynamics root-finding

2021-10-01abs ↗pdf ↗

Coarse-Graining Auto-Encoders for Molecular Dynamicsphysics.chem-ph

Auto-encoders learn atomistic to coarse-grained mappings for molecular dynamics.

problem Simulating large systems in molecular dynamics is computationally expensive.

method Auto-encoders learn both atomistic to coarse-grained mappings and the coarse-grained potential energy function.

result Auto-encoders enable efficient simulation of larger systems in molecular dynamics.

molecular dynamics coarse-graining auto-encoders force-matching statistical mechanics

2018-12-06abs ↗pdf ↗

Coarse-Grained Boltzmann Generatorscs.LG

CG-BGs combine flow-based models with PMFs to sample large systems efficiently.

problem Sampling equilibrium molecular configurations from the Boltzmann distribution is challenging.

method Coarse-grained Boltzmann Generators (CG-BGs) use flow-based models and learned PMFs for efficient sampling.

result CG-BGs provide a practical route for sampling larger molecular systems efficiently.

Boltzmann Generators Coarse-graining Flow-based models PMFs Equilibrium sampling

2026-02-11abs ↗pdf ↗

Machine Learning of coarse-grained Molecular Dynamics Force Fieldsphysics.comp-ph

Machine learning improves coarse-graining of molecular dynamics models.

problem Creating accurate coarse-grained models for molecular dynamics simulations.

method Reformulated coarse-graining as a supervised machine learning problem using statistical learning theory and deep learning (CGnets).

result CGnets can capture multi-body terms and all-atom explicit-solvent free energy surfaces with fewer coarse-grained beads.

machine learning coarse-graining molecular dynamics force fields statistical learning

2018-12-04abs ↗pdf ↗

Coarse Graining Molecular Dynamics with Graph Neural Networksphysics.comp-ph

Machine learning generates coarse-grained force fields for molecular dynamics.

problem Creating thermodynamically consistent coarse-grained models for larger systems.

method Hybrid architecture using graph neural networks to learn molecular features.

result Framework reproduces thermodynamics for small biomolecular systems.

coarse graining molecular dynamics graph neural networks force fields thermodynamics

2020-07-22abs ↗pdf ↗