Multicomponent Fe-Cr-based alloys. Machine learning

NIAID Data Ecosystem2026-03-12 收录

合金材料性能预测

机器学习辅助材料设计

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https://data.mendeley.com/datasets/y6hsd3nzkc 数据链接链接失效反馈

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资源简介：

We predicted the mixing enthalpy, Young’s modulus and Pugh’s ratio for over 0.5 million multicomponent Fe-Cr-based alloys.

创建时间：

2021-07-15

Elucidating the Physicochemical Basis of the Glass Transition Temperature in Linear Polyurethane Elastomers with Machine Learning

高分子材料性能预测

机器学习辅助材料设计

The glass transition temperature (Tg) is a fundamental property of polymers that strongly influences both mechanical and flow characteristics of the material. In many important polymers, configuration

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材料多尺度优化

机器学习辅助材料设计

Optimizing materials is crucial for advancing modern technologies, requiring analysis from atomic to microstructural scales. Traditional methods, including trial-and-error experiments and standard com

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Machine Learning Prediction for Electronic Density of States in Guanine-TiO2 ...

材料电子结构计算

机器学习辅助材料设计

This dataset houses a research poster and its poster abstract. The set of documents was first presented at the doctoral days organized by the Doctoral Committee of the...

B2FIND00

Machine-Learning-Enabled Thermochemistry Estimator

热化学预测

机器学习辅助材料设计

Modeling adsorbates on single-crystal metals is critical in rational catalyst design and other research that requires detailed thermochemistry. First-principles simulations via density functional theo

Figshare2024-12-16 更新10

Raw data set for "Understanding and Control of Zener Pinning via Phase Field and Ensemble Learning"

材料微观模拟

机器学习辅助材料设计

Phase field generated Raw data set for "Understanding and Control of Zener Pinning via Phase Field and Ensemble Learning"

Zenodo2023-07-07 更新00