Pallikara Ioanna, Skelton Jonathan M, Hatcher Lauren E, Pallipurath Anuradha R
School of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, U.K.
Department of Chemistry, University of Manchester, Manchester M13 9PL, U.K.
Cryst Growth Des. 2024 Aug 19;24(17):6911-6930. doi: 10.1021/acs.cgd.4c00694. eCollection 2024 Sep 4.
When Olga Kennard founded the Cambridge Crystallographic Data Centre in 1965, the Cambridge Structural Database was a pioneering attempt to collect scientific data in a standard format. Since then, it has evolved into an indispensable resource in contemporary molecular materials science, with over 1.25 million structures and comprehensive software tools for searching, visualizing and analyzing the data. In this perspective, we discuss the use of the CSD and CCDC tools to address the multiscale challenge of predictive materials design. We provide an overview of the core capabilities of the CSD and CCDC software and demonstrate their application to a range of materials design problems with recent case studies drawn from topical research areas, focusing in particular on the use of data mining and machine learning techniques. We also identify several challenges that can be addressed with existing capabilities or through new capabilities with varying levels of development effort.
1965年，奥尔加·凯纳德创立了剑桥晶体学数据中心，彼时的剑桥结构数据库是一项以标准格式收集科学数据的开创性尝试。自那时起，它已发展成为当代分子材料科学中不可或缺的资源，拥有超过125万个结构以及用于搜索、可视化和分析数据的综合软件工具。在本文中，我们讨论了如何使用CSD和CCDC工具来应对预测性材料设计中的多尺度挑战。我们概述了CSD和CCDC软件的核心功能，并通过从热门研究领域选取的近期案例研究，展示了它们在一系列材料设计问题中的应用，特别关注数据挖掘和机器学习技术的使用。我们还确定了一些可以通过现有功能或通过开发工作量不同的新功能来应对的挑战。
