Data and training checkpoints of BTO

NIAID Data Ecosystem2026-05-01 收录

铁电材料表征

材料性能预测

数据链接：

https://zenodo.org/record/10092320 数据链接链接失效反馈

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资源简介：

Bright field images and training checkpoints of freestanding BTO in various temperatures and chemical environments

创建时间：

2023-11-09

Materials Data on CaSO6 by Materials Project

晶体结构解析

材料性能预测

CaSO4O2 crystallizes in the monoclinic P2_1/c space group. The structure is two-dimensional and consists of four hydrogen peroxide molecules and two CaSO4 sheets oriented in the (0, 1, 0) direction. I

DataCite Commons2021-02-04 更新110

Predicting the Structure Stability of Layered Heteroanionic Materials Exhibiting Anion Order

材料性能预测

异阴离子材料设计

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50

GRACE-2L-OAM-L: WBM relaxed structures

材料结构优化

材料性能预测

WBM structures relaxed with GRACE-2L-OAM-L model for Matbench Discovery test

Figshare2025-09-09 更新20

Data from Prediction for Mechanical Properties of Lean Duplex Stainless Steel by Using Random Forest

材料性能预测

材料机器学习

Lean duplex stainless steel (LDSS), which is expected to apply to infrastructures, exhibits rounded shape of stress-strain curve. For this reason, a constitutive equation which is able to accurately e

NIAID Data Ecosystem70

Database on ANN-Based Performance Evaluation of Composite Recycled Mortar

材料性能预测

人工神经网络

The recycling of construction waste is crucial for environmental preservation and sustainable progress. By integrating construction waste into mortar production, substantial reductions in carbon emiss

科学数据银行2025-07-11 更新50

Data and training checkpoints of BTO

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