OCELOT: An infrastructure for data-driven research to discover and design crystalline organic semiconductors
OCELOT: An infrastructure for data-driven research to discover and design crystalline organic semiconductors
复制标题
DOI：
10.1063/5.0048714
复制
发表时间：
2021-05-07
影响因子：
4.4
通讯作者：
Risko, Chad
中科院分区：
文献类型：
--
作者：
Ai, Qianxiang;Bhat, Vinayak;Risko, Chad
材料的设计和发现往往受到缓慢的速度和材料和人力成本与爱迪生的试错筛选方法。然而，计算能力、理论方法和数据科学技术的最新进展正体现在这些工具的融合中，以实现计算机材料的发现。在这里，我们介绍了晶体有机半导体的计算材料数据和数据分析方法的开发和部署。OCELOT（Organic Crystals in Electronic and Light-Oriented Technologies）基础设施由基于Python的OCELOT应用程序编程接口和OCELOT数据库组成，旨在实现快速材料探索。该数据库包含了一个基于生物传感器的高通量计算模式，该模式已在来自47000个不同分子结构的56000多个实验晶体结构上实现。OCELOT是开放的，可通过https://oscar.as.uky.edu的网络用户界面访问。由AIP Publishing授权出版。
Materials design and discovery are often hampered by the slow pace and materials and human costs associated with Edisonian trial-and-error screening approaches. Recent advances in computational power, theoretical methods, and data science techniques, however, are being manifest in a convergence of these tools to enable in silico materials discovery. Here, we present the development and deployment of computational materials data and data analytic approaches for crystalline organic semiconductors. The OCELOT (Organic Crystals in Electronic and Light-Oriented Technologies) infrastructure, consisting of a Python-based OCELOT application programming interface and OCELOT database, is designed to enable rapid materials exploration. The database contains a descriptor-based schema for high-throughput calculations that have been implemented on more than 56 000 experimental crystal structures derived from 47 000 distinct molecular structures. OCELOT is open-access and accessible via a web-user interface at https://oscar.as.uky.edu. Published under license by AIP Publishing.