Imperfections are not 0 K: free energy of point defects in crystals
Defects determine many important properties and applications of materials, ranging from
do** in semiconductors, to conductivity in mixed ionic–electronic conductors used in …
do** in semiconductors, to conductivity in mixed ionic–electronic conductors used in …
Machine-learning potentials for crystal defects
Decades of advancements in strategies for the calculation of atomic interactions have
culminated in a class of methods known as machine-learning interatomic potentials …
culminated in a class of methods known as machine-learning interatomic potentials …
Calculation of dislocation binding to helium-vacancy defects in tungsten using hybrid ab initio-machine learning methods
Calculations of dislocation-defect interactions are essential to model metallic strength, but
the required system sizes are at or beyond ab initio limits. Current estimates thus have …
the required system sizes are at or beyond ab initio limits. Current estimates thus have …
An approach to evaluate the accuracy of interatomic potentials as applied to tungsten
IV Kosarev, SA Shcherbinin, AA Kistanov… - Computational Materials …, 2024 - Elsevier
Molecular dynamics (MD) is a powerful tool for modeling structural transformations in
metallic materials under irradiation, severe plastic deformation, laser processing, etc. The …
metallic materials under irradiation, severe plastic deformation, laser processing, etc. The …
Defect modeling and control in structurally and compositionally complex materials
Conventional computational approaches for modeling defects face difficulties when applied
to complex materials, mainly due to the vast configurational space of defects. In this …
to complex materials, mainly due to the vast configurational space of defects. In this …
[HTML][HTML] Efficiency, accuracy, and transferability of machine learning potentials: Application to dislocations and cracks in iron
Abstract Machine learning interatomic potentials (ML-IAPs) enable quantum-accurate,
classical molecular dynamics simulations of large systems, beyond reach of density …
classical molecular dynamics simulations of large systems, beyond reach of density …
Vacancy-induced phonon localization in boron arsenide using a unified neural network interatomic potential
Boron arsenide, considered an ideal semiconductor, inevitably introduces arsenic defects
during crystal growth. Here, we develop a unified neural network interatomic potential with …
during crystal growth. Here, we develop a unified neural network interatomic potential with …
A tungsten deep neural-network potential for simulating mechanical property degradation under fusion service environment
Tungsten is a promising candidate material in fusion energy facilities. Molecular dynamics
(MD) simulations reveal the atomistic scale mechanisms, so they are crucial for the …
(MD) simulations reveal the atomistic scale mechanisms, so they are crucial for the …
Multiscale machine-learning interatomic potentials for ferromagnetic and liquid iron
A large and increasing number of different types of interatomic potentials exist, either based
on parametrised analytical functions or machine learning. The choice of potential to be used …
on parametrised analytical functions or machine learning. The choice of potential to be used …
Prediction of stable Li-Sn compounds: boosting ab initio searches with neural network potentials
Abstract The Li-Sn binary system has been the focus of extensive research because it
features Li-rich alloys with potential applications as battery anodes. Our present re …
features Li-rich alloys with potential applications as battery anodes. Our present re …