首页/数据集/Estimating Phase Boundaries Using Sampling and Machine Learning of Derivatives of Thermochemistry Properties

Estimating Phase Boundaries Using Sampling and Machine Learning of Derivatives of Thermochemistry Properties

NIAID Data Ecosystem2026-05-02 收录

图计算

热力学性质预测

数据链接：

https://data.mendeley.com/datasets/9ff7mxf7k5 数据链接链接失效反馈

官方服务：

资源简介：

The data contain the original thermodynamic data of the two systems, which can be calculated according to the content of the article.

应用场景：

创建时间：

2025-06-05

热力学性质预测

基团贡献法

A rigorous methodology is developed that addresses numerical and statistical issues when developing group contribution (GC) based property models such as regression methods, optimization algorithms, p

NIAID Data Ecosystem70

Thermodynamics assessment of the Erbium-Ruthenium system by combining the Ab-initio and CALPHAD methods

图计算

合金热力学评估

In the present study, the erbium-ruthenium system was optimised using the CALPHAD approach. The thermodynamic properties and the phase diagram were evaluated. The enthalpies of formation of the interm

Taylor & Francis Group2025-03-18 更新40

Modeling the thermodynamic properties of saturated lactones in non-ideal mixtures with the SAFT-γ Mie approach. JCED 2023

热力学性质预测

相平衡建模

All computational data in the publication.

Zenodo2023-08-21 更新30

Data underlying the publication: Brute Force Composition Scanning with a CALPHAD Database to Find Low Temperature Body Centered Cubic High Entropy Alloys

难熔高熵合金

图计算

High entropy alloys with predictable mechanical properties by computational modelling.Scripts for running Thermocalc over multicomponent alloys over large compositions grid as function of temperature.

4TU.ResearchData2023-07-07 更新50

Graph-based deep learning models for thermodynamic property prediction: The interplay between target definition, data distribution, featurization, and model architecture

热力学性质预测

图神经网络

This folder contains the formation energy of BDE-db, QM9, PC9, QMugs, and QMugs1.1 datasets by filtering (The training, test, and validation sets were randomly split in a ratio of 0.8, 0.1, and 0.1, r

Figshare2024-10-30 更新30

Estimating Phase Boundaries Using Sampling and Machine Learning of Derivatives of Thermochemistry Properties

资源简介：

相关数据集