| Literature DB >> 36074116 |
Masato Sumita1,2, Kei Terayama1,3, Ryo Tamura1,2,4,5, Koji Tsuda1,4,5.
Abstract
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.Entities:
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Year: 2022 PMID: 36074116 PMCID: PMC9518232 DOI: 10.1021/acs.jcim.2c00812
Source DB: PubMed Journal: J Chem Inf Model ISSN: 1549-9596 Impact factor: 6.162