RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan.
International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science, Tsukuba 305-0044, Japan.
J Chem Inf Model. 2022 Sep 26;62(18):4427-4434. doi: 10.1021/acs.jcim.2c00812. Epub 2022 Sep 8.
To obtain observable physical or molecular properties such as ionization potential and fluorescent wavelength with quantum chemical (QC) computation, multi-step computation manipulated by a human is required. Hence, automating the multi-step computational process and making it a black box that can be handled by anybody are important for effective database construction and fast realistic material design through the framework of black-box optimization where machine learning algorithms are introduced as a predictor. Here, we propose a Python library, QCforever, to automate the computation of some molecular properties and chemical phenomena induced by molecules. This tool just requires a molecule file for providing its observable properties, automating the computation process of molecular properties (for ionization potential, fluorescence, etc.) and output analysis for providing their multi-values for evaluating a molecule. Incorporating the tool in black-box optimization, we can explore molecules that have properties we desired within the limitation of QC computation.
为了通过引入机器学习算法作为预测器的黑盒优化框架来获得可观测的物理或分子性质,如电离势和荧光波长,需要进行多步骤的计算,这些计算由人类进行操作。因此,自动化多步骤计算过程并使其成为任何人都可以处理的黑盒对于通过黑盒优化有效地构建数据库和快速实现实际材料设计非常重要。在这里,我们提出了一个 Python 库 QCforever,用于自动化一些分子性质和分子诱导的化学现象的计算。该工具只需要一个分子文件即可提供其可观测性质,自动化分子性质(如电离势、荧光等)的计算过程,并输出分析结果,以提供其多值来评估分子。将该工具纳入黑盒优化中,我们可以在 QC 计算的限制范围内探索具有我们所需性质的分子。