官方服务:
资源简介:
Deciphering the Composition-Process-Microstructure Correlation in Low-Density FeMnAlC Steels with Machine Learning
应用场景:
创建时间:
2024-03-08
相关数据集
Materials Data on Mg6BC by Materials Project
Mg6BC crystallizes in the orthorhombic Amm2 space group. The structure is two-dimensional and consists of two Mg6BC sheets oriented in the (0, 1, 0) direction. there are four inequivalent Mg sites. In
DataCite Commons2021-02-04 更新100
Materials Data on Li4Ti2Co3Sn3O16 by Materials Project
Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For
DataCite Commons2021-02-04 更新70
Materials Data on Hf3Hg by Materials Project
Hf3Hg is Uranium Silicide-like structured and crystallizes in the tetragonal I4/mmm space group. The structure is three-dimensional. there are two inequivalent Hf sites. In the first Hf site, Hf is bo
DataCite Commons2021-02-04 更新50
Materials Data on BaNa6Th(CO4)6 by Materials Project
Na6BaTh(CO4)6 crystallizes in the trigonal R-3 space group. The structure is three-dimensional. Na is bonded in a 8-coordinate geometry to eight O atoms. There are a spread of Na–O bond distances rang
DataCite Commons2021-02-04 更新90
Nonlocal machine-learned exchange functional for molecules and solids
This dataset supplements the journal article "Nonlocal machine-learned exchange functional for molecules and solids," published in Physical Review B: DOI:10.1103/PhysRevB.110.075130. It contains the m
NIAID Data Ecosystem50



