Manganese steel machine learning

Mendeley Data2026-04-18 收录

材料设计优化

机器学习材料预测

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资源简介：

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.

创建时间：

2022-04-08

Accelerated Discovery of Novel Garnet-Type Solid-State Electrolyte Candidates via Machine Learning

固态电解质

机器学习材料预测

All-solid-state batteries (ASSBs) have attracted considerable attention because of their higher energy density and stability than conventional lithium-ion batteries (LIBs). For the development of prom

NIAID Data Ecosystem50

Importance of Feature Selection in the Adaptive Design of Materials by Machine Learning

材料设计优化

机器学习特征选择

This dataset contains features (combined, ionic radii, and electronegativity) and band gap (Eg) values for apatites in their lowest energy structure.

NIAID Data Ecosystem50

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,

NIAID Data Ecosystem30

Beyond the BET Analysis: The Surface Area Prediction of Nanoporous Materials Using a Machine Learning Method

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 Ecosystem50

Million-Fold Electrical Conductivity Enhancement in Fe2(DEBDC) versus Mn2(DEBDC) (E = S, O)

金属有机框架电化学

材料设计优化

Reaction of FeCl2 and H4DSBDC (2,5-disulfhydrylbenzene-1,4-dicarboxylic acid) leads to the formation of Fe2(DSBDC), an analogue of M2(DOBDC) (MOF-74, DOBDC4– = 2,5-dihydroxybenzene-1,4-dicarbox

Figshare2015-12-17 更新40

Manganese steel machine learning

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