Construction of machine-learning Zr interatomic potentials for identifying the formation process of c-type dislocation loops

2022 ◽  
Vol 202 ◽  
pp. 110865
Author(s):  
T. Okita ◽  
S. Terayama ◽  
K. Tsugawa ◽  
K. Kobayashi ◽  
M. Okumura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Formation Process ◽  
Interatomic Potentials ◽  
Dislocation Loops ◽  
Type Dislocation

scholarly journals A transferable active-learning strategy for reactive molecular force fields

2021 ◽  
Author(s):  
Tom Young ◽  
Tristan Johnston-Wood ◽  
Volker L. Deringer ◽  
Fernanda Duarte
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Learning Strategy ◽  
Force Fields ◽  
Molecular Simulations ◽  
Quantum Mechanical ◽  
Interatomic Potentials ◽  
Molecular Force ◽  
High Level ◽  
Active Learning Strategy

Predictive molecular simulations require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct such potentials by fitting energies and forces to high-level quantum-mechanical data, but...

The behavior of vacancy-type dislocation loops under electron irradiation in iron

2014 ◽  
Vol 455 (1-3) ◽  
pp. 253-257 ◽  
Author(s):  
Farong Wan ◽  
Qian Zhan ◽  
Yi Long ◽  
Shanwu Yang ◽  
Gaowei Zhang ◽  
...  
Keyword(s):  
Electron Irradiation ◽  
Dislocation Loops ◽  
Type Dislocation

The Mechanical Modeling of Oxygen-Containing Precipitates in Silicon Wafers on Different Stages of the Getter Formation Process

2011 ◽  
Vol 178-179 ◽  
pp. 483-488
Author(s):  
Pavel S. Shushpannikov ◽  
Robert V. Goldstein ◽  
Konstantin Ustinov
Keyword(s):  
Experimental Data ◽  
Silicon Wafer ◽  
Formation Process ◽  
Strain State ◽  
Spherical Shape ◽  
Point Of View ◽  
Dislocation Loops ◽  
Mechanical Modeling ◽  
Silicon Matrix ◽  
Spheroidal Inclusion

The behavior of the oxygen-containing precipitate in silicon wafer on different stages of the getter formation process is considered from the mechanical point of view. The precipitate is modeled as a spheroidal inclusion undergoing inelastic eigenstrains in an anisotropic silicon matrix. The stress-strain state in the precipitate and matrix is calculated within the framework of the model. An energetic criterion of breaking the spherical shape by the coherent precipitates is obtained and analyzed. Criteria of the formation and onset of motion of the dislocation loops in the vicinity of the precipitate are also proposed. The obtained results are compared with the available experimental data.

scholarly journals Taking materials dynamics to new extremes using machine learning interatomic potentials

2021 ◽  
Author(s):  
Yang Yang ◽  
Long Zhao ◽  
Chen-Xu Han ◽  
Xiang-Dong Ding ◽  
Turab Lookman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Interatomic Potentials

scholarly journals In-situ study of one-dimensional motion of interstitial-type dislocation loops in hydrogen-ion-implanted aluminum

2022 ◽  
Vol 71 (1) ◽  
pp. 016102-016102
Author(s):  
Li Ran-Ran ◽  
◽  
Zhang Yi-Fan ◽  
Yin Yu-Peng ◽  
Watanabe Hideo ◽  
...  
Keyword(s):  
Hydrogen Ion ◽  
Dislocation Loops ◽  
One Dimensional ◽  
Type Dislocation ◽  
Ion Implanted

Dual adaptive sampling and machine learning interatomic potentials for modeling materials with chemical bond hierarchy

2021 ◽  
Vol 104 (9) ◽  
Author(s):  
Hongliang Yang ◽  
Yifan Zhu ◽  
Erting Dong ◽  
Yabei Wu ◽  
Jiong Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chemical Bond ◽  
Adaptive Sampling ◽  
Interatomic Potentials

Machine Learning Interatomic Potentials for Global Optimization and Molecular Dynamics Simulation

2019 ◽  
pp. 253-288 ◽  
Author(s):  
Ivan A. Kruglov ◽  
Pavel E. Dolgirev ◽  
Artem R. Oganov ◽  
Arslan B. Mazitov ◽  
Sergey N. Pozdnyakov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Global Optimization ◽  
Molecular Dynamics Simulation ◽  
Dynamics Simulation ◽  
Interatomic Potentials

Accessing thermal conductivity of complex compounds by machine learning interatomic potentials

2019 ◽  
Vol 100 (14) ◽  
Author(s):  
Pavel Korotaev ◽  
Ivan Novoselov ◽  
Aleksey Yanilkin ◽  
Alexander Shapeev
Keyword(s):  
Machine Learning ◽  
Thermal Conductivity ◽  
Complex Compounds ◽  
Interatomic Potentials

scholarly journals Performance and Cost Assessment of Machine Learning Interatomic Potentials

2020 ◽  
Vol 124 (4) ◽  
pp. 731-745 ◽  
Author(s):  
Yunxing Zuo ◽  
Chi Chen ◽  
Xiangguo Li ◽  
Zhi Deng ◽  
Yiming Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cost Assessment ◽  
Interatomic Potentials
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