GNN_EVAL

Name: GNN_EVAL
Creator: Okabe, Ryotaro
Published: 2024-11-22T00:00:00+08:00

Figshare2024-11-22 更新2026-04-08 收录

材料性能预测

图神经网络评估

数据链接：

https://figshare.com/articles/dataset/GNN_EVAL/27887097/1 数据链接链接失效反馈

官方服务：

资源简介：

We provide the training dataset for GNN_EVAL, the model to prescreen the materials generated by SCIGEN.

提供机构：

Okabe, Ryotaro

创建时间：

2024-11-22

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二维材料计算

材料性能预测

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Database on ANN-Based Performance Evaluation of Composite Recycled Mortar

材料性能预测

人工神经网络

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NbCrVWZr Graph Database Analysis Files

合金优化

材料性能预测

This dataset contains derived composition–property data for candidate alloys in the Nb–Cr–V–W–Zr system. Each data entry includes elemental composition, two-dimensional UMAP coordinates, and selected

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Materials Data on Li4Ti2Co3Sn3O16 by Materials Project

计算材料学

材料性能预测

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

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二维铁电材料

铁电材料设计

材料性能预测

铁电材料数据集主要源自高通量计算和文献挖掘，涵盖从第一性原理计算到实验表征的多层次信息：例如基于Materials Project自动筛选出的数百种新型铁电体、从4万余篇论文中提取的近三千条高质量铁电相变数据，以及针对特定材料（如铪基、PZT陶瓷、二维多铁）的精细实验数据。近年来，这些数据已与深度学习、交互式平台深度融合，催生出如FerroAI（20秒生成相图、预测准确率超80%）和PTST极化拓

中国科学院兰州化学物理研究所科学数据中心2026-06-08 更新50

GNN_EVAL

资源简介：

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