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The glass transition in the perspective of the machine learning

NIAID Data Ecosystem2026-05-01 收录

材料玻璃化转变

机器学习材料预测

数据链接：

https://data.mendeley.com/datasets/385nsgjvkm 数据链接链接失效反馈

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资源简介：

The dataset of "The glass transition in the perspective of the machine learning"

应用场景：

创建时间：

2023-11-22

多孔材料表征

机器学习材料预测

Surface areas of porous materials such as metal–organic frameworks (MOFs) are commonly characterized using the Brunauer–Emmett–Teller (BET) method. However, it has been shown that the BET method does

NIAID Data Ecosystem80

Data for "Machine Learning Predictions of Irradiation Embrittlement in Reactor Pressure Vessel Steels"

核材料辐照损伤

机器学习材料预测

To ensure all publicly available data used in this paper are easily accessible and adequately archived, we have placed the following files in the SI and on Figshare with DOI 10.6084/m9.figshare.128164

Figshare2022-04-07 更新40

Manganese steel machine learning

材料设计优化

机器学习材料预测

We have collected data on the composition, treatment process and properties of Mn steel, and proposed a material design model through machine learning, which is currently being submitted.

Mendeley Data30

Rheological Data

材料流变特性

机器学习材料预测

The rheological data for the manuscript of POA-DELM

Figshare2024-07-16 更新70

Machine Learning Tools to Predict Hot Injection Syntheses Outcomes for II–VI and IV–VI Quantum Dots

量子点合成优化

机器学习材料预测

In order to allow quantum dots with the desired physical and chemical properties, the fine control and prediction of size during chemical syntheses is a challenge that must be addressed. In this work,

Figshare2020-10-22 更新40

The glass transition in the perspective of the machine learning

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