Image de couverture de livre

A general formalism for machine-learning models based on multipolar-spherical harmonics

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Domina, Michelangelo, Sanvito, Stefano
Format:	Preprint
Publié:	2025
Sujets:	Chemical Physics Materials Science Computational Physics
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

How unconstrained machine-learning models learn physical symmetries
par: Domina, Michelangelo, et autres
Publié: (2026)

Representing spherical tensors with scalar-based machine-learning models
par: Domina, Michelangelo, et autres
Publié: (2025)

A machine-learning framework for accelerating spin-lattice relaxation simulations
par: Briganti, Valerio, et autres
Publié: (2024)

Massive Atomic Diversity: a compact universal dataset for atomistic machine learning
par: Mazitov, Arslan, et autres
Publié: (2025)

Can machines learn density functionals? Past, present, and future of ML in DFT
par: Akashi, Ryosuke, et autres
Publié: (2025)

Overcoming the chemical complexity bottleneck in on-the-fly machine learned molecular dynamics simulations
par: Timmerman, Lucas R., et autres
Publié: (2024)

Accelerating point defect simulations using data-driven and machine learning approaches
par: Mannodi-Kanakkithodi, Arun, et autres
Publié: (2026)

Revealing the molecular structures of a-Al2O3(0001)-water interface by machine learning based computational vibrational spectroscopy
par: Du, Xianglong, et autres
Publié: (2024)

A universal machine learning model for the electronic density of states
par: How, Wei Bin, et autres
Publié: (2025)

Revealing the proton slingshot mechanism in solid acid electrolytes through machine learning molecular dynamics
par: Wang, Menghang, et autres
Publié: (2025)

Bridging electronic and classical density-functional theory using universal machine-learned functional approximations
par: Kelley, Michelle M., et autres
Publié: (2024)

A charge-density machine-learning workflow for computing the infrared spectrum of molecules
par: Hazra, Suman, et autres
Publié: (2025)

Modelling complex proton transport phenomena -- Exploring the limits of fine-tuning and transferability of foundational machine-learned force fields
par: Grunert, Malte, et autres
Publié: (2025)

DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations
par: Li, Yang, et autres
Publié: (2026)

Adaptive energy reference for machine-learning models of the electronic density of states
par: How, Wei Bin, et autres
Publié: (2024)

A foundation machine learning potential with polarizable long-range interactions for materials modelling
par: Gao, Rongzhi, et autres
Publié: (2024)

Efficient real space formalism for hybrid density functionals
par: Jing, Xin, et autres
Publié: (2024)

Molecular electrostatic potentials from machine learning models for dipole and quadrupole predictions
par: Muuga, Kadri, et autres
Publié: (2026)

Latent Ewald summation for machine learning of long-range interactions
par: Cheng, Bingqing
Publié: (2024)

Universal rapid machine learning models for predicting unconvoluted and convoluted X-ray Absorption Spectra
par: Zhan, Fei, et autres
Publié: (2026)

A machine-learned kinetic energy model for light weight metals and compounds of group III-V elements
par: Lüder, Johann, et autres
Publié: (2024)

Cyclic- and helical-symmetry-adapted phonon formalism within density functional perturbation theory
par: Sharma, Abhiraj, et autres
Publié: (2026)

MBD-ML: Many-body dispersion from machine learning for molecules and materials
par: Moerman, Evgeny, et autres
Publié: (2026)

Self-learning path integral hybrid Monte Carlo with mixed ab initio and machine learning potentials for modeling nuclear quantum effects in water
par: Thomsen, Bo, et autres
Publié: (2024)

Unveiling the amorphous ice layer during premelting using AFM integrating machine learning
par: Tang, Binze, et autres
Publié: (2025)

Long-range electrostatics for machine learning interatomic potentials is easier than we thought
par: Kim, Dongjin, et autres
Publié: (2025)

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture
par: Brabson, Logan M., et autres
Publié: (2025)

A dual-cutoff machine-learned potential for condensed organic systems obtained via uncertainty-guided active learning
par: Kahle, Leonid, et autres
Publié: (2024)

High-quality, high-information datasets for universal atomistic machine learning
par: Malosso, Cesare, et autres
Publié: (2026)

Point defect formation at finite temperatures with machine learning force fields
par: Mosquera-Lois, Irea, et autres
Publié: (2024)

A unified machine learning framework for ab initio multiscale modeling of liquids
par: Bui, Anna T., et autres
Publié: (2026)

Covariant Jacobi-Legendre expansion for total energy calculations within the projector-augmented-wave formalism
par: Focassio, Bruno, et autres
Publié: (2024)

Fast Evaluation of Unbiased Atomic Forces in ab initio Variational Monte Carlo via the Lagrangian Technique
par: Nakano, Kousuke, et autres
Publié: (2025)

Reaction dynamics of lithium-mediated electrolyte decomposition using machine learning potentials
par: Kundu, Sohang, et autres
Publié: (2025)

Errors that matter: Uncertainty-aware universal machine-learning potentials calibrated on experiments
par: Kellner, Matthias, et autres
Publié: (2026)

Predicting aqueous and electrochemical stability of 2D materials from extended Pourbaix analyses
par: Americo, Stefano, et autres
Publié: (2025)

Machine learning classification of local environments in molecular crystals
par: Kuroshima, Daisuke, et autres
Publié: (2024)

PAL -- Parallel active learning for machine-learned potentials
par: Zhou, Chen, et autres
Publié: (2024)

Machine learning the non-radiative decay modes in photochemical processes
par: Banerjee, Debarshi, et autres
Publié: (2026)

Generating new coordination compounds via multireference simulations, genetic algorithms and machine learning: the case of Co(II) molecular magnets
par: Frangoulis, Lion, et autres
Publié: (2025)