Image de couverture de livre

Adaptive energy reference for machine-learning models of the electronic density of states

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	How, Wei Bin, Chong, Sanggyu, Grasselli, Federico, Huguenin-Dumittan, Kevin K., Ceriotti, Michele
Format:	Preprint
Publié:	2024
Sujets:	Materials Science Chemical Physics
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

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

Comparing the latent features of universal machine-learning interatomic potentials
par: Chorna, Sofiia, et autres
Publié: (2025)

Robustness of Local Predictions in Atomistic Machine Learning Models
par: Chong, Sanggyu, et autres
Publié: (2023)

Fast and flexible long-range models for atomistic machine learning
par: Loche, Philip, et autres
Publié: (2024)

Uncertainty in the era of machine learning for atomistic modeling
par: Grasselli, Federico, et autres
Publié: (2025)

Completeness of Atomic Structure Representations
par: Nigam, Jigyasa, et autres
Publié: (2023)

Prediction rigidities for data-driven chemistry
par: Chong, Sanggyu, et autres
Publié: (2024)

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

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

Resolving the Body-Order Paradox of Machine Learning Interatomic Potentials
par: Chong, Sanggyu, et autres
Publié: (2025)

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

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

FlashMD: long-stride, universal prediction of molecular dynamics
par: Bigi, Filippo, et autres
Publié: (2025)

Smooth, exact rotational symmetrization for deep learning on point clouds
par: Pozdnyakov, Sergey N., et autres
Publié: (2023)

Learning the action for long-time-step simulations of molecular dynamics
par: Bigi, Filippo, et autres
Publié: (2025)

Mechanistic study of mixed lithium halides solid state electrolytes
par: Tisi, Davide, 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 machine-learned kinetic energy model for light weight metals and compounds of group III-V elements
par: Lüder, Johann, et autres
Publié: (2024)

Thermal conductivity of Li$_3$PS$_4$ solid electrolytes with ab initio accuracy
par: Tisi, Davide, et autres
Publié: (2024)

Mechanism of charge transport in lithium thiophosphate
par: Gigli, Lorenzo, et autres
Publié: (2023)

Modeling the structural and thermal properties of loaded metal-organic frameworks. An interplay of quantum and anharmonic fluctuations
par: Kapil, Venkat, et autres
Publié: (2019)

A prediction rigidity formalism for low-cost uncertainties in trained neural networks
par: Bigi, Filippo, et autres
Publié: (2024)

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

Reducing cross-sample prediction churn in scientific machine learning
par: Prastalo, Gordan, et autres
Publié: (2026)

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

Ab initio thermodynamics of liquid and solid water
par: Cheng, Bingqing, et autres
Publié: (2018)

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

Topological descriptors for the electron density of inorganic solids
par: Szymanski, Nathan J., et autres
Publié: (2025)

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

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)

PET-MAD, a lightweight universal interatomic potential for advanced materials modeling
par: Mazitov, Arslan, et autres
Publié: (2025)

The dark side of the forces: assessing non-conservative force models for atomistic machine learning
par: Bigi, Filippo, et autres
Publié: (2024)

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)

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

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

Reaction dynamics of lithium-mediated electrolyte decomposition using machine learning potentials
par: Kundu, Sohang, 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)

Foundations of the ionization potential condition for localized electron removal in density functional theory
par: Ohad, Guy, et autres
Publié: (2025)

Temperature- and charge carrier density-dependent electronic response in methylammonium lead iodide
par: Park, Jiacheng Wang Jungmin, et autres
Publié: (2025)

Long-range electrostatics in atomistic machine learning: a physical perspective
par: Grasselli, Federico, et autres
Publié: (2026)