Cover Image

Accelerating point defect simulations using data-driven and machine learning approaches

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Mannodi-Kanakkithodi, Arun, Huang, Menglin, Gorai, Prashun, Kavanagh, Seán R.
Format:	Preprint
Published:	2026
Subjects:	Materials Science Chemical Physics Computational Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Machine-learning structural reconstructions for accelerated point defect calculations
by: Mosquera-Lois, Irea, et al.
Published: (2024)

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

Imperfections are not 0 K: free energy of point defects in crystals
by: Mosquera-Lois, Irea, et al.
Published: (2023)

doped: Python toolkit for robust and repeatable charged defect supercell calculations
by: Kavanagh, Seán R., et al.
Published: (2024)

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

A machine-learning framework for accelerating spin-lattice relaxation simulations
by: Briganti, Valerio, et al.
Published: (2024)

Point defect formation at finite temperatures with machine learning force fields
by: Mosquera-Lois, Irea, et al.
Published: (2024)

Bridging electronic and classical density-functional theory using universal machine-learned functional approximations
by: Kelley, Michelle M., et al.
Published: (2024)

Massive Atomic Diversity: a compact universal dataset for atomistic machine learning
by: Mazitov, Arslan, et al.
Published: (2025)

A general formalism for machine-learning models based on multipolar-spherical harmonics
by: Domina, Michelangelo, et al.
Published: (2025)

Can machines learn density functionals? Past, present, and future of ML in DFT
by: Akashi, Ryosuke, et al.
Published: (2025)

Revealing the proton slingshot mechanism in solid acid electrolytes through machine learning molecular dynamics
by: Wang, Menghang, et al.
Published: (2025)

Unveiling the amorphous ice layer during premelting using AFM integrating machine learning
by: Tang, Binze, et al.
Published: (2025)

How unconstrained machine-learning models learn physical symmetries
by: Domina, Michelangelo, et al.
Published: (2026)

Modelling complex proton transport phenomena -- Exploring the limits of fine-tuning and transferability of foundational machine-learned force fields
by: Grunert, Malte, et al.
Published: (2025)

Generating new coordination compounds via multireference simulations, genetic algorithms and machine learning: the case of Co(II) molecular magnets
by: Frangoulis, Lion, et al.
Published: (2025)

Unsupervised identification of crystal defects from atomistic potential descriptors
by: Kývala, Lukáš, et al.
Published: (2024)

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

Reaction dynamics of lithium-mediated electrolyte decomposition using machine learning potentials
by: Kundu, Sohang, et al.
Published: (2025)

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

Unsupervised learning of representative local atomic arrangements in molecular dynamics data
by: Roncoroni, Fabrice, et al.
Published: (2023)

Machine learned potential for defected single layer hexagonal boron nitride
by: Janisch, John, et al.
Published: (2025)

Accelerating point defect photo-emission calculations with machine learning interatomic potentials
by: Sharma, Kartikeya, et al.
Published: (2025)

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

Long-range electrostatics for machine learning interatomic potentials is easier than we thought
by: Kim, Dongjin, et al.
Published: (2025)

A universal machine learning model for the electronic density of states
by: How, Wei Bin, et al.
Published: (2025)

How reproducible are first-principles simulations of liquid water?
by: ONeill, Niamh, et al.
Published: (2026)

Accelerated Inorganic Electrides Discovery by Generative Models and Hierarchical Screening
by: Tao, Shuo, et al.
Published: (2026)

Solute diffusion by self-interstitial defects and radiation-induced segregation in ferritic Fe-X (X=Cr, Cu, Mn, Ni, P, Si) dilute alloys
by: Messina, Luca, et al.
Published: (2019)

High-quality, high-information datasets for universal atomistic machine learning
by: Malosso, Cesare, et al.
Published: (2026)

Adaptive energy reference for machine-learning models of the electronic density of states
by: How, Wei Bin, et al.
Published: (2024)

Variational Lang-Firsov approach plus Møller-Plesset perturbation theory with applications to ab initio polariton chemistry
by: Cui, Zhi-Hao, et al.
Published: (2023)

Beyond theory driven discovery: hot random search and datum derived structures
by: Pickard, Chris J.
Published: (2024)

Machine Learning Accelerated Descriptor Design for Catalyst Discovery in CO$_2$ to Methanol Conversion
by: Pisal, Prajwal, et al.
Published: (2024)

Molecular electrostatic potentials from machine learning models for dipole and quadrupole predictions
by: Muuga, Kadri, et al.
Published: (2026)

Errors that matter: Uncertainty-aware universal machine-learning potentials calibrated on experiments
by: Kellner, Matthias, et al.
Published: (2026)

A dual-cutoff machine-learned potential for condensed organic systems obtained via uncertainty-guided active learning
by: Kahle, Leonid, et al.
Published: (2024)

Insights into $CO_{2}$ activation on defective ZnS surfaces
by: de Oliveira, P. R. A, et al.
Published: (2026)

Machine learning classification of local environments in molecular crystals
by: Kuroshima, Daisuke, et al.
Published: (2024)

Machine learning accelerated finite-field simulations for electrochemical interfaces
by: Feng, Chaoqiang, et al.
Published: (2025)