Cargando…
Exploration of Solid Solutions and the Strengthening of Aluminum Substrates by Alloying Atoms: Machine Learning Accelerated Density Functional Theory Calculations
In this paper, we studied the effects of a series of alloying atoms on the stability and micromechanical properties of aluminum alloy using a machine learning accelerated first-principles approach. In our preliminary work, high-throughput first-principles calculations were explored and the solution...
Autores principales: | Huang, Jingtao, Xue, Jingteng, Li, Mingwei, Cheng, Yuan, Lai, Zhonghong, Hu, Jin, Zhou, Fei, Qu, Nan, Liu, Yong, Zhu, Jingchuan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10608410/ https://www.ncbi.nlm.nih.gov/pubmed/37895739 http://dx.doi.org/10.3390/ma16206757 |
Ejemplares similares
-
Accelerating First Principles Calculation of Multi-Component Alloy Steady-State Structure and Elastic Properties in Full Component Space
por: Yao, Zhixuan, et al.
Publicado: (2023) -
Effect of Temperatures and Graphene on the Mechanical Properties of the Aluminum Matrix: A Molecular Dynamics Study
por: Huang, Jingtao, et al.
Publicado: (2023) -
Accelerating Density Functional Calculation of Adatom Adsorption on Graphene via Machine Learning
por: Qu, Nan, et al.
Publicado: (2023) -
Freezing solute atoms in nanograined aluminum alloys via high-density vacancies
por: Wu, Shenghua, et al.
Publicado: (2022) -
Preparation of Textured Surfaces on Aluminum-Alloy Substrates
por: Kadlečková, Markéta, et al.
Publicado: (2018)