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MEGNET-mp-2019-04-01 JARVIS-Atoms format

Figshare2021-06-07 更新2026-04-28 收录

材料性能预测

材料信息学

数据链接：

https://figshare.com/articles/dataset/MEGNET-mp-2019-04-01_JARVIS-Atoms_format/14745435 数据链接链接失效反馈

官方服务：

资源简介：

Megnet training dataset (mp-2019-04-01 ) with pymatgen structures converted into jarvis.core.Atoms

应用场景：

创建时间：

2021-06-07

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

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

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

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

Engineering Machine Learning features to predict adsorption of carbon dioxide and nitrogen in metal-organic frameworks

金属有机框架吸附性能预测

材料信息学

This repository contains CIF files for metal-organic frameworks and Grand canonical Monte Carlo (GCMC) simulation results for the article Engineering Machine Learning features to predict adsorption of

NIAID Data Ecosystem40

MEGNET-mp-2019-04-01 JARVIS-Atoms format

资源简介：

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