超越订购量:关于使用剑桥结构数据库用于预测材料设计的观点
Ioanna Pallikara1, Jonathan M Skelton2, Lauren E Hatcher3
1School of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, U.K.
Crystal growth & design
|September 9, 2024
概括
剑桥结构数据库 (CSD) 及其工具对于预测性材料设计至关重要. 这些资源利用数据挖掘和机器学习来推进分子材料科学.
科学领域:
- 分子材料科学科学 分子材料科学
- 晶体学 晶体学是指结晶学.
- 数据科学数据科学数据科学
背景情况:
- 剑桥结构数据库 (CSD) 成立于1965年,开创了标准化的科学数据收集.
- 它已经发展成为一个重要的资源,拥有超过125万个结构和先进的分析软件.
- 剑桥晶体数据中心 (CCDC) 为数据管理和分析提供了全面的工具.
研究的目的:
- 讨论CSD和CCDC工具用于预测材料设计的应用.
- 突出数据挖掘和机器学习在材料科学中的应用.
- 通过使用CSD和CCDC的能力,识别材料设计当前和未来的挑战.
主要方法:
- 使用剑桥结构数据库 (CSD) 进行结构数据的处理.
- 使用剑桥晶体数据中心 (CCDC) 软件进行搜索,可视化和分析.
- 将数据挖掘和机器学习技术应用于材料设计问题.
主要成果:
- 展示了CSD和CCDC工具在解决材料设计中的多层面挑战方面的实用性.
- 介绍了当前研究领域的案例研究,展示了实际应用.
- 确定了现有的或新的CCDC能力可以增强材料设计的特定领域.
结论:
- CSD和CCDC工具是推动预测材料设计的强大资源.
- 数据挖掘和机器学习的整合为未来的材料发现提供了巨大的潜力.
- 持续发展CCDC的能力将进一步解决分子材料科学的复杂挑战.
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