Copertina

Broadening the Scope of Neural Network Potentials through Direct Inclusion of Additional Molecular Attributes

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Simeon, Guillem, Mirarchi, Antonio, Pelaez, Raul P., Galvelis, Raimondas, De Fabritiis, Gianni
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Chemical Physics Computational Physics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations
di: Pelaez, Raul P., et al.
Pubblicazione: (2024)

AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics
di: Mirarchi, Antonio, et al.
Pubblicazione: (2024)

Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials
di: Zariquiey, Francesc Sabanes, et al.
Pubblicazione: (2024)

Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations
di: De Fabritiis, Gianni
Pubblicazione: (2024)

AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules
di: Farr, Stephen E., et al.
Pubblicazione: (2026)

QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials
di: Zariquiey, Francesc Sabanés, et al.
Pubblicazione: (2025)

Thermodynamics-Informed Accurate pKa Prediction and Protonation State Generation in PlayMolecule AI
di: Pesce, Francesco, et al.
Pubblicazione: (2026)

Molecular dynamics simulation with finite electric fields using Perturbed Neural Network Potentials
di: Joll, Kit, et al.
Pubblicazione: (2024)

Impact of Local Descriptors Derived from Machine Learning Potentials in Graph Neural Networks for Molecular Property Prediction
di: Uchiyama, Ryoichi, et al.
Pubblicazione: (2026)

Refining Potential Energy Surface through Dynamical Properties via Differentiable Molecular Simulation
di: Han, Bin, et al.
Pubblicazione: (2024)

Efficient Parallelization of Message Passing Neural Network Potentials for Large-scale Molecular Dynamics
di: Xia, Junfan, et al.
Pubblicazione: (2025)

The Bigger the Better? Accurate Molecular Potential Energy Surfaces from Minimalist Neural Networks
di: Käser, Silvan, et al.
Pubblicazione: (2024)

Dichotomy between Level Broadening and Level Coupling to Electrodes in Large Area EGaIn Molecular Junctions
di: Baldea, Ioan
Pubblicazione: (2024)

Visualization-Based Approach to Condensed-Phase Line Broadening Using Polyene Chains
di: Mahmoodpour, Saba, et al.
Pubblicazione: (2026)

NeuralTSNE: A Python Package for the Dimensionality Reduction of Molecular Dynamics Data Using Neural Networks
di: Tajs, Patryk, et al.
Pubblicazione: (2025)

Beyond the Static Approximation: Assessing the Impact of Conformational and Kinetic Broadening on the Description of TADF Emitters
di: Beer, Daniel, et al.
Pubblicazione: (2026)

Molecular Dynamics Simulations of $γ$-Belite(010)-Water Interfaces with High-Dimensional Neural Network Potentials
di: Prus, Bernadeta, et al.
Pubblicazione: (2025)

Egret-1: Pretrained Neural Network Potentials for Efficient and Accurate Bioorganic Simulation
di: Mann, Elias L., et al.
Pubblicazione: (2025)

Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Non-Conservative Forces
di: Gouraud, Nicolaï, et al.
Pubblicazione: (2026)

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

Neural Network Approach for Predicting Infrared Spectra from 3D Molecular Structure
di: Al, Saleh Abdul, et al.
Pubblicazione: (2024)

Neural Network Based Molecular Structure Retrieval from Coulomb Explosion Imaging Data
di: Ghanaatian, Amirhossein, et al.
Pubblicazione: (2026)

Local Pseudopotential Unlocks the True Potential of Neural Network-based Quantum Monte Carlo
di: Fu, Weizhong, et al.
Pubblicazione: (2025)

Explicit, Machine-Learned Two-Body Potentials for Molecular Simulations
di: Chaton, Kham Lek, et al.
Pubblicazione: (2026)

Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models using Multiple Time-Step and Distillation
di: Cattin, Côme, et al.
Pubblicazione: (2025)

Scalable Hybrid Deep Neural Networks/Polarizable Potentials Biomolecular Simulations including long-range effects
di: Inizan, Théo Jaffrelot, et al.
Pubblicazione: (2022)

Neural Network Potential with Multi-Resolution Approach Enables Accurate Prediction of Reaction Free Energies in Solution
di: Pultar, Felix, et al.
Pubblicazione: (2024)

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

Neural Network Potential Energy Surface for the low temperature Ring Polymer Molecular Dynamics of the H2CO + OH reaction
di: del Mazo-Sevillano, Pablo, et al.
Pubblicazione: (2021)

A Simple and Efficient Equivariant Message Passing Neural Network Model for Non-Local Potential Energy Surface
di: Wu, Yibin, et al.
Pubblicazione: (2024)

Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations
di: Eastman, Peter, et al.
Pubblicazione: (2026)

Molecular Quantum Chemical Data Sets and Databases for Machine Learning Potentials
di: Ullah, Arif, et al.
Pubblicazione: (2024)

Transferable Water Potentials Using Equivariant Neural Networks
di: Maxson, Tristan, et al.
Pubblicazione: (2024)

Quantum Dynamical and isotopic effects for Hydrogen isotopes scattering at W(110) surface
di: Bombín, Raúl, et al.
Pubblicazione: (2026)

Double Ionization Potential Equation-of-Motion Coupled-Cluster Approach with Full Inclusion of 4-Hole-2-Particle Excitations and Three-Body Clusters
di: Gururangan, Karthik, et al.
Pubblicazione: (2024)

Toward Routine CSP of Pharmaceuticals: A Fully Automated Protocol Using Neural Network Potentials
di: Glick, Zachary L., et al.
Pubblicazione: (2025)

Moment Tensor Potential and Equivariant Tensor Network Potential with explicit dispersion interactions
di: Chalykh, Olga, et al.
Pubblicazione: (2025)

Implicit Delta Learning of High Fidelity Neural Network Potentials
di: Thaler, Stephan, et al.
Pubblicazione: (2024)

Enhanced Deep Potential Model for Fast and Accurate Molecular Dynamics; Application to the Hydrated Electron
di: Gao, Ruiqi, et al.
Pubblicazione: (2024)

QMCTorch: Molecular Wavefunctions with Neural Components for Energy and Force Calculations
di: Renaud, Nicolas
Pubblicazione: (2025)