Imagen de Portada

Density-Based Long-Range Electrostatic Descriptors for Machine Learning Force Fields

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Faller, Carolin, Kaltak, Merzuk, Kresse, Georg
Formato:	Preprint
Publicado:	2024
Materias:	Materials Science
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Efficient periodic density functional theory calculations of charged molecules and surfaces using Coulomb kernel truncation
por: Vijay, Sudarshan, et al.
Publicado: (2025)

Machine Learning Study of Surface Reconstructions of the Cu$_{2}$O(111) Surface
por: Wadhwa, Payal, et al.
Publicado: (2025)

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties
por: Weber, John L., et al.
Publicado: (2025)

Derivative learning of tensorial quantities -- Predicting finite temperature infrared spectra from first principles
por: Schmiedmayer, Bernhard, et al.
Publicado: (2024)

Constrained Random Phase Approximation: the spectral method
por: Kaltak, Merzuk, et al.
Publicado: (2025)

ADAPT: Lightweight, Long-Range Machine Learning Force Fields Without Graphs
por: Dramko, Evan, et al.
Publicado: (2025)

Dynamical Disorder in the Mesophase Ferroelectric HdabcoClO4: A Machine-Learned Force Field Study
por: Sødahl, Elin Dypvik, et al.
Publicado: (2024)

Learning the Electrostatic Response of the Electron Density through a Symmetry-Adapted Vector Field Model
por: Rossi, Mariana, et al.
Publicado: (2025)

Scalar machine learning of tensorial quantities -- Born effective charges from monopole models
por: Schmiedmayer, Bernhard, et al.
Publicado: (2026)

Machine Learning Force Field for Thermal Oxidation of Silicon
por: Cvitkovich, Lukas, et al.
Publicado: (2024)

Long-Range Machine Learning of Electron Density for Twisted Bilayer Moiré Materials
por: Lou, Zekun, et al.
Publicado: (2026)

Comparative analysis of methods for calculating Hubbard parameters using cRPA
por: Reddy, Indukuru Ramesh, et al.
Publicado: (2025)

Tracing Ion Migration in Halide Perovskites with Machine Learned Force Fields
por: Tyagi, Viren, et al.
Publicado: (2024)

Selectivity- and Activity-Aware Catalyst Descriptors for CO$_2$ Hydrogenation on Alloy Nanocatalysts using Machine-Learned Force Fields
por: Pisal, Prajwal, et al.
Publicado: (2026)

Finite-Temperature Ferroelectric Phase Transitions from Machine-Learned Force Fields
por: Eggestad, Kristoffer, et al.
Publicado: (2025)

Equivariant machine learning of Electric Field Gradients -- Predicting the quadrupolar coupling constant in the MAPbI$_3$ phase transition
por: Schmiedmayer, Bernhard, et al.
Publicado: (2025)

On-the-Fly Machine-Learned Force Fields for High-Fidelity Polymer Glass Transition Simulations
por: Srivastava, Ashutosh, et al.
Publicado: (2026)

A Perspective on Training Machine Learning Force Fields for Solid-State Electrolyte Materials
por: Yan, Zihan, et al.
Publicado: (2026)

Strength of 2D Glasses Explored by Machine-Learning Force Fields
por: Shi, Pengjie, et al.
Publicado: (2024)

Evaluating Universal Machine Learning Force Fields Against Experimental Measurements
por: Mannan, Sajid, et al.
Publicado: (2025)

Toward Multi-Fidelity Machine Learning Force Field for Cathode Materials
por: Dong, Guangyi, et al.
Publicado: (2025)

Phase Stability and Transformations in Lead Mixed Halide Perovskites from Machine Learning Force Fields
por: Liang, Xia, et al.
Publicado: (2025)

Machine-Learning Force Fields Reveal Shallow Electronic States on Dynamic Halide Perovskite Surfaces
por: Delgado, Frederico P., et al.
Publicado: (2025)

Descriptor and Graph-based Molecular Representations in Prediction of Copolymer Properties Using Machine Learning
por: Kazemi-Khasragh, Elaheh, et al.
Publicado: (2025)

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties
por: Wines, Daniel, et al.
Publicado: (2024)

Machine Learned Force Fields: Fundamentals, its reach, and challenges
por: Vital, Carlos A., et al.
Publicado: (2025)

Elucidating the High-Pressure Phases of MAPbBr3 Using a Machine Learning Force Field
por: Valappil, Rashid Rafeek V, et al.
Publicado: (2025)

Towards Universal Material Property Prediction with Deep Learning and Single-Descriptor electronic Density
por: Chen, Feng, et al.
Publicado: (2025)

Theoretical Studies on Sodium Storage Mechanism in Hard Carbon Anodes of Sodium-Ion Batteries: Molecular Simulations Based on Machine Learning Force Fields
por: Wang, Zhaoming, et al.
Publicado: (2024)

On Simulating Thin-Film Processes at the Atomic Scale Using Machine Learned Force Fields
por: Natarajan, S. Kondati, et al.
Publicado: (2025)

Understanding and Mitigating Distribution Shifts For Machine Learning Force Fields
por: Kreiman, Tobias, et al.
Publicado: (2025)

Ab initio calculations of the thermoelectric figure of merit, within the relaxation time approximation
por: Chaput, Laurent, et al.
Publicado: (2025)

Two-Scale Analysis of the Electrostatics of Dielectric Crystals: Emergence of Polarization Density and Boundary Charges
por: Sen, Shoham, et al.
Publicado: (2026)

Evaluating First-Principles Electron-Phonon Couplings: Consistency Across Methods and Implementations
por: Merkel, Konrad, et al.
Publicado: (2025)

Machine learned Force-Fields for an ab-initio Quality Description of Metal-Organic Frameworks
por: Wieser, Sandro, et al.
Publicado: (2023)

Linear Scaling Calculation of Atomic Forces and Energies with Machine Learning Local Density Matrix
por: Xin, Zaizhou, et al.
Publicado: (2025)

Imaging the Quantum Capacitance of Strained MoS2 Monolayers by Electrostatic Force Microscopy
por: Di Giorgio, Cinzia, et al.
Publicado: (2023)

Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy
por: Schönbauer, Sita, et al.
Publicado: (2025)

Neutron and X-ray Diffraction Reveal the Limits of Long-Range Machine Learning Potentials for Medium-Range Order in Silica Glass
por: Balantrapu, Sai Harshit, et al.
Publicado: (2026)

Identifying Split Vacancy Defects with Machine-Learned Foundation Models and Electrostatics
por: Kavanagh, Seán R.
Publicado: (2024)