随机松弛数据集用于锂硅系统的晶体结构搜索
收藏arXiv2023-03-09 更新2024-07-24 收录
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https://research.google/pubs/crystal-structure-search-with-random-structure-relaxations-using-graph-networks/
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资源简介：
本数据集由斯坦福大学和Google Research合作创建，包含超过100,000个潜在电池阳极材料的结构松弛，使用密度泛函理论计算从随机结构中生成。数据集旨在通过训练图神经网络预测随机生成的结构的结构松弛，以加速晶体结构搜索。该数据集适用于锂离子电池等领域的研究，旨在解决材料设计中的晶体结构预测问题。
Collaboratively developed by Stanford University and Google Research, this dataset contains over 100,000 structural relaxations of potential battery anode materials, generated from random structures via Density Functional Theory (DFT) calculations. This dataset aims to accelerate crystal structure search by training Graph Neural Networks (GNNs) to predict the structural relaxations of randomly generated structures. It is applicable to research in fields such as lithium-ion batteries, and is designed to address the crystal structure prediction challenge in materials design.
提供机构：
斯坦福大学
创建时间：
2020-12-05
