Imagen de Portada

Analyzing Atomic Interactions in Molecules as Learned by Neural Networks

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Esders, Malte, Schnake, Thomas, Lederer, Jonas, Kabylda, Adil, Montavon, Grégoire, Tkatchenko, Alexandre, Müller, Klaus-Robert
Formato:	Preprint
Publicado:	2024
Materias:	Chemical Physics
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

How Atoms Interact Within Molecules
por: Kabylda, Adil, et al.
Publicado: (2026)

Towards Linearly Scaling and Chemically Accurate Global Machine Learning Force Fields for Large Molecules
por: Kabylda, Adil, et al.
Publicado: (2022)

MBD-ML: Many-body dispersion from machine learning for molecules and materials
por: Moerman, Evgeny, et al.
Publicado: (2026)

QCell: Comprehensive Quantum-Mechanical Dataset Spanning Diverse Biomolecular Fragments
por: Kabylda, Adil, et al.
Publicado: (2025)

Are Molecules Magical? Non-Stabilizerness in Molecular Bonding
por: Sarkis, Matthieu, et al.
Publicado: (2025)

A Transferable Model of Molecular Exchange-Repulsion Interaction from Anisotropic Valence Density Overlap
por: Wing, Dahvyd, et al.
Publicado: (2025)

Two Protons, Two Positrons, and Four Electrons: Covalent Bond with van der Waals Characteristics
por: Charry, Jorge, et al.
Publicado: (2026)

Quantum-Accurate Conformational Stabilities and Vibrational Dynamics in Molecules and Proteins with Machine-Learned Force Fields
por: Suárez-Dou, Sergio, et al.
Publicado: (2026)

Quantum Field Approaches to Chemical Systems
por: Karimpour, Reza, et al.
Publicado: (2026)

Machine Learning Force Fields
por: Unke, Oliver T., et al.
Publicado: (2020)

Normalized Relevance Measure as a Unifying Framework to Explain Neural Network Latent Structures
por: Xiong, Ping, et al.
Publicado: (2026)

aims-PAX: Parallel Active eXploration for the automated construction of Machine Learning Force Fields
por: Henkes, Tobias, et al.
Publicado: (2025)

Machine-Learned Electrostatic Potentials for Accurate Hydration Free Energy Calculations
por: Hilfiker, Mathias, et al.
Publicado: (2025)

Force-Field-Enhanced Neural Network Interactions: from Local Equivariant Embedding to Atom-in-Molecule properties and long-range effects
por: Plé, Thomas, et al.
Publicado: (2023)

Relevant Walk Search for Explaining Graph Neural Networks
por: Xiong, Ping, et al.
Publicado: (2026)

Machine Learning Multiscale Interactions
por: Solé, Àlex, et al.
Publicado: (2026)

QMeCha: quantum Monte Carlo package for fermions in embedding environments
por: Barborini, Matteo, et al.
Publicado: (2025)

Mechanical Properties of Nanoporous Graphenes: Transferability of Graph Machine‐Learned Force Fields Compared to Local and Reactive Potentials
por: Adil Kabylda, et al.
Publicado: (2024)

$Δ$-Machine Learning to Elevate DFT-based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol
por: Nandi, Apurba, et al.
Publicado: (2024)

Accurate quantum Monte Carlo forces for machine-learned force fields: Ethanol as a benchmark
por: Slootman, Emiel, et al.
Publicado: (2024)

TERS-ABNet: A Deep Learning Approach for Automated Single-Molecule Structure Reconstruction with Atomic Precision from TERS Mapping
por: Cui, Jie, et al.
Publicado: (2026)

A Phase-Space Electronic Hamiltonian for Molecules in a Static Magnetic Field II: Quantum Chemistry Calculations with Gauge Invariant Atomic Orbitals
por: Bhati, Mansi, et al.
Publicado: (2024)

Uncovering the Structure of Explanation Quality with Spectral Analysis
por: Maeß, Johannes, et al.
Publicado: (2025)

Efficient Higher-order Subgraph Attribution via Message Passing
por: Xiong, Ping, et al.
Publicado: (2026)

Natural-Orbital-Based Neural Network Configuration Interaction
por: Thirion, Louis, et al.
Publicado: (2025)

Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features
por: Schnake, Thomas, et al.
Publicado: (2024)

GemNet: Universal Directional Graph Neural Networks for Molecules
por: Gasteiger, Johannes, et al.
Publicado: (2021)

Complete and Efficient Covariants for 3D Point Configurations with Application to Learning Molecular Quantum Properties
por: Maennel, Hartmut, et al.
Publicado: (2024)

Temperature-Enhanced Coercive Field by Chiral Molecules
por: Kapon, Yael, et al.
Publicado: (2024)

Orbital Optimization and Neural-Network-Assisted Configuration Interaction Calculations of Rydberg States
por: Levi, Gianluca, et al.
Publicado: (2025)

Singlet Fission among two Single Molecules
por: Paul, Sumanta, et al.
Publicado: (2025)

Quantum-Electrodynamical Time-Dependent Density Functional Theory Description of Molecules Interacting with Light
por: Aklilu, Yetmgeta, et al.
Publicado: (2026)

Beyond Learning on Molecules by Weakly Supervising on Molecules
por: Prastalo, Gordan, et al.
Publicado: (2026)

Neural Canonical Transformation for the Spectra of Fluxional Molecule CH5+
por: Wang, Ruisi, et al.
Publicado: (2025)

A Neural-Network-Based Selective Configuration Interaction Approach to Molecular Electronic Structure
por: Schmerwitz, Yorick L. A., et al.
Publicado: (2024)

The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials
por: Xia, Junfan, et al.
Publicado: (2025)

Screening in the Heitler-London Model: Revisiting the Bonding and Antibonding States of the Hydrogen Molecule
por: da Silva, Washington P., et al.
Publicado: (2025)

Selective Excitation of Vibrations in a Single Molecule
por: Luo, Yang, et al.
Publicado: (2024)

Scattering Angle Dependence of Fano Resonance Profiles in Cold Atomic Collisions Analyzed with the Complex Valued $w$ Parameter
por: Singh, Tanmay, et al.
Publicado: (2025)

From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields
por: Frank, J. Thorben, et al.
Publicado: (2023)