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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.

168,695 papers · 148 categories

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4 results for “gas-phase”

Probing the effects of broken symmetries in machine learningphysics.chem-ph

A machine learning model with approximate rotational symmetry is tested and found stable.

problem The effects of broken symmetries in machine learning models.

method Testing a model with approximate rotational symmetry in various physical scenarios.

result The model remains stable even with noticeable symmetry artifacts, suggesting potential benefits.

machine learning symmetry breaking rotational invariance gas-phase liquid

2024-06-25abs ↗pdf ↗

A universal phase-field mixture representation of thermodynamics and shock wave mechanics in porous soft biologic continuacond-mat.soft

Universal model for soft tissue mechanics under shock waves.

problem Modeling shock wave mechanics in soft biological tissues.

method Continuum mixture theory with phase-field mechanics.

result Universal thermodynamically consistent formulation for soft porous tissues.

soft tissues phase-field mechanics shock waves thermodynamics continuum mechanics

2024-03-08abs ↗pdf ↗

Orders-of-magnitude speedup in atmospheric chemistry modeling through neural network-based emulationphysics.ao-ph

Chemical transport models (CTMs), which simulate air pollution transport, transformation, and removal, are computationally expensive, largely because of the computational intensity of the chemical mechanisms: systems of coupled differential equations representing atmospheric chemistry. Here we investigate the potential…

2018-08-11abs ↗pdf ↗

Enhancing molecular dynamics with equivariant machine-learned densitiesphysics.chem-ph

DenSNet learns electron densities for molecular dynamics, enabling accurate spectroscopic predictions.

problem Lack of accurate electronic observables in MLIPs for molecular dynamics.

method DenSNet uses SE(3)-equivariant neural networks to predict electron densities and total energy.

result DenSNet predicts infrared spectra with excellent agreement to experimental data.

machine learning molecular dynamics electron density interatomic potentials equivariant neural networks

2026-04-27abs ↗pdf ↗