Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining
Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining
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DOI：
10.1021/acs.jpcc.9b01147
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发表时间：
2019-06-13
影响因子：
3.7
通讯作者：
Hachmann, Johannes
中科院分区：
文献类型：
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作者：
Afzal, Mohammad Atif Faiz;Haghighatlari, Mojtaba;Hachmann, Johannes
我们提出了一项高通量计算研究，以确定具有特殊折射率（RI）值的新型聚酰亚胺（pi），用于光学或光电材料。我们的研究利用了一种基于第一性原理和先前工作中开发的数据建模相结合的RI预测协议，我们在ChemLG代码生成的大规模PI候选库上使用了该协议。我们部署了虚拟筛选软件ChemHTPS来自动评估这些广泛的PI结构池，以确定每个候选人的性能潜力。这种快速有效的方法产生了许多极有前途的先导化合物。使用数据挖掘和机器学习程序包ChemML，我们根据流行的结构特征和特征组合来分析最热门的候选者，以区分它们与不太有希望的候选者。特别是，我们探索了各种策略的效用，这些策略将高度极化的部分引入PI主干以提高其RI产率。由此产生的见解为理性和有针对性的设计提供了基础，超越了传统的试错搜索。
We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising lead compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.