Shen Jimmy-Xuan, Munro Jason M, Horton Matthew K, Huck Patrick, Dwaraknath Shyam, Persson Kristin A
Department of Materials Science and Engineering, University of California, Berkeley, Berkeley, California, 94720, USA.
Lawrence Livermore National Laboratory, Livermore, USA.
Sci Data. 2022 Oct 28;9(1):661. doi: 10.1038/s41597-022-01746-z.
In addition to being the core quantity in density-functional theory, the charge density can be used in many tertiary analyses in materials sciences from bonding to assigning charge to specific atoms. The charge density is data-rich since it contains information about all the electrons in the system. With the increasing prevalence of machine-learning tools in materials sciences, a data-rich object like the charge density can be utilized in a wide range of applications. The database presented here provides a modern and user-friendly interface for a large and continuously updated collection of charge densities as part of the Materials Project. In addition to the charge density data, we provide the theory and code for changing the representation of the charge density which should enable more advanced machine-learning studies for the broader community.
除了作为密度泛函理论的核心量之外，电荷密度还可用于材料科学中的许多二级分析，从化学键合到为特定原子分配电荷。电荷密度包含的数据丰富，因为它包含了系统中所有电子的信息。随着机器学习工具在材料科学中的日益普及，像电荷密度这样数据丰富的对象可用于广泛的应用。本文介绍的数据库为作为材料项目一部分的大量且不断更新的电荷密度集合提供了一个现代且用户友好的界面。除了电荷密度数据之外，我们还提供了用于改变电荷密度表示形式的理论和代码，这应该能使更广泛的群体开展更先进的机器学习研究。
