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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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10 results for “atom-centered”

Unified theory of atom-centered representations and message-passing machine-learning schemesstat.ML

Unified theory linking atom-centered and message-passing models for molecular properties.

problem Combining atom-centered and message-passing models for accurate molecular property prediction.

method Generalizing ACDC framework to include multi-centered information, providing a complete linear basis for regression.

result Unified understanding of atom-centered and message-passing models, providing a coherent foundation.

Unified theory atom-centered message-passing machine-learning molecular properties

2022-02-03abs ↗pdf ↗

WACSF - Weighted Atom-Centered Symmetry Functions as Descriptors in Machine Learning Potentialsphysics.chem-ph

We introduce weighted atom-centered symmetry functions (wACSFs) as descriptors of a chemical system's geometry for use in the prediction of chemical properties such as enthalpies or potential energies via machine learning. The wACSFs are based on conventional atom-centered symmetry functions (ACSFs) but overcome the un…

2017-12-15abs ↗pdf ↗

Equivariant representations for molecular Hamiltonians and N-center atomic-scale propertiesphysics.chem-ph

New features for quantum calculations learn N-center Hamiltonian matrix elements.

problem Quantum calculations need features for N-center Hamiltonians, not just atom-centered ones.

method Developed fully equivariant N-center features for machine learning.

result Learned matrix elements of N-center Hamiltonians efficiently.

equivariant features N-center Hamiltonian machine learning quantum calculations symmetry

2021-09-24abs ↗pdf ↗

Electronic-structure properties from atom-centered predictions of the electron densityphysics.chem-ph

A new method predicts electron density accurately from atom-centered models.

problem Predicting electron density accurately from atom-centered models.

method Gradient-based approach to minimize loss function in an optimized sparse feature space.

result Extremely accurate predictions of electron density and total energies.

electron density machine learning density fitting Kohn-Sham total energy

2022-06-28abs ↗pdf ↗

ML4Chem: A Machine Learning Package for Chemistry and Materials Sciencephysics.chem-ph

ML4Chem offers a user-friendly platform for developing and deploying machine learning models in chemistry.

problem Developing and deploying machine learning models in chemistry and materials science.

method User-experience design, six core building blocks: data, featurization, models, model optimization, inference, and visualization.

result Ease of use and functionality of the atomistic module for neural networks and kernel ridge regression.

machine learning chemistry materials science user experience open source

2020-03-02abs ↗pdf ↗

Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Sciencephysics.chem-ph

MACE architecture outperforms alternatives in various molecular and materials science tasks.

problem Improving machine learning force fields for diverse molecular and materials science applications.

method Evaluation of MACE architecture on various datasets and tasks, demonstrating data efficiency and excellent performance.

result MACE architecture generally outperforms alternatives across a wide range of systems, including amorphous carbon, universal materials modelling, and organic chemistry.

MACE architecture machine learning force fields materials science molecular dynamics data efficiency

2023-05-23abs ↗pdf ↗

The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentialsstat.ML

Unified framework for machine learning interatomic potentials.

problem Designing and optimizing machine learning models for interatomic potentials.

method Unified mathematical framework unifying ACE and NequIP, providing a systematic design space.

result Demonstrated through ablation studies, critical design choices for high accuracy.

Machine Learning Interatomic Potentials Equivariance ACE NequIP

2022-05-13abs ↗pdf ↗

A data-driven interpretation of the stability of molecular crystalsphysics.chem-ph

Machine learning predicts molecular crystal stability.

problem Predicting the stability of molecular crystals.

method Supervised and unsupervised machine learning techniques to classify and predict lattice energy.

result Data-driven assessment of chemical groups' contribution to crystal stability.

machine learning molecular crystals crystal engineering structural descriptors lattice energy

2022-09-21abs ↗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 ↗

Learning the electronic density of states in condensed mattercond-mat.mtrl-sci

Machine learning predicts electronic density of states for condensed matter.

problem Predicting the electronic density of states (DOS) in complex condensed matter systems.

method Developed a machine learning framework to predict DOS from density functional theory data, considering geometric configurations of atoms.

result Demonstrated the model's effectiveness in predicting DOS and its components for various silicon configurations.

Electronic density of states Machine learning Condensed matter Density functional theory Thermodynamic conditions

2020-06-21abs ↗pdf ↗