Data for: VOLCO: a predictive model for 3D printed microarchitecture
收藏Mendeley Data2026-04-18 收录
官方服务:
资源简介:
Data for individual samples is given
应用场景:
创建时间:
2018-04-27
相关数据集
Predicting Mohs Hardness of Minerals Using Machine Learning on Atomic Descriptors
Predicting the hardness of a material is a challenging task due to the complex relationships between the atomic properties and the mohs hardness. To tackle this challenge, we leveraged machine learn
Zenodo2026-04-20 更新10
Predicting the Structure Stability of Layered Heteroanionic Materials Exhibiting Anion Order
We report a workflow for heteroanionic materials discovery using Pauling’s second rule to filter for and predict new candidate materials for synthesis with reduced computational overhead. Using oxyflu
Figshare2019-09-17 更新10
Materials Data on V3As2O9 by Materials Project
V3As2O9 crystallizes in the tetragonal P4bm space group. The structure is two-dimensional and consists of one V3As2O9 sheet oriented in the (0, 0, 1) direction. there are two inequivalent V5+ sites. I
DataCite Commons2021-02-04 更新30
Friction Coefficient Data and Prediction Using Decision Tree Model for Open-Cell AlSi10Mg-SiC Composites Under Dry-Sliding Condition
The materials used in the data files are: open-cell AlSi10Mg materials and open-cell AlSi10Mg-SiC composites with pore sizes in a range of 1000 – 1200 μm fabricated by liquid-state-processing. The mat
Mendeley Data10



