AQME:研究人员和教育工作者的自动化量子力学环境

IF 16.8 2区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2023-02-26 DOI:10.1002/wcms.1663
Juan V. Alegre-Requena, Shree Sowndarya S. V., Raúl Pérez-Soto, Turki M. Alturaifi, Robert S. Paton
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引用次数: 3

摘要

AQME,自动化量子力学环境,是一个免费的开源Python包,用于使用化学信息学和量子化学快速部署自动化工作流程。AQME工作流集成了跨多种计算化学包和数据格式执行的任务,保留了所有计算协议、数据和元数据,供机器和人类用户访问和重用。AQME具有独立模块的模块化结构,可以按任何顺序实现,允许用户使用程序的所有或仅所需部分。该代码是为基本熟悉Python编程语言的研究人员开发的。CSEARCH模块从各种初始结构格式开始,与分子力学和半经验QM(SQM)构象器生成工具(如RDKit和conformer–Rotamer Ensemble Sampling Tool,CREST)对接。CMIN模块能够利用SQM和神经网络电位(如ANI)进行几何细化。QPREP模块与多个QM程序接口,例如Gaussian、ORCA和PySCF。QCORR模块处理QM结果,存储结构、能量和特性数据,同时实现自动错误处理(即收敛错误、虚频错误数量、异构化等)和作业重新提交。QDESCP模块提供了对QM系综平均分子描述符和计算性质(如NMR光谱)的方便访问。总体而言,AQME提供了自动化、透明和可复制的工作流程,用于生成、分析和归档计算化学结果。可以使用SMILES输入,并且可以避免繁琐的人工操作的许多方面。已经测试了Windows、macOS和Linux平台上的安装和执行,并开发了代码以支持通过Jupyter Notebooks、命令行和作业提交(例如Slurm)脚本进行访问。预配置工作流的示例有多种格式,实践视频教程演示了它们的使用。本文分类如下:
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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AQME: Automated quantum mechanical environments for researchers and educators

AQME, automated quantum mechanical environments, is a free and open-source Python package for the rapid deployment of automated workflows using cheminformatics and quantum chemistry. AQME workflows integrate tasks performed across multiple computational chemistry packages and data formats, preserving all computational protocols, data, and metadata for machine and human users to access and reuse. AQME has a modular structure of independent modules that can be implemented in any sequence, allowing the users to use all or only the desired parts of the program. The code has been developed for researchers with basic familiarity with the Python programming language. The CSEARCH module interfaces to molecular mechanics and semi-empirical QM (SQM) conformer generation tools (e.g., RDKit and Conformer–Rotamer Ensemble Sampling Tool, CREST) starting from various initial structure formats. The CMIN module enables geometry refinement with SQM and neural network potentials, such as ANI. The QPREP module interfaces with multiple QM programs, such as Gaussian, ORCA, and PySCF. The QCORR module processes QM results, storing structural, energetic, and property data while also enabling automated error handling (i.e., convergence errors, wrong number of imaginary frequencies, isomerization, etc.) and job resubmission. The QDESCP module provides easy access to QM ensemble-averaged molecular descriptors and computed properties, such as NMR spectra. Overall, AQME provides automated, transparent, and reproducible workflows to produce, analyze and archive computational chemistry results. SMILES inputs can be used, and many aspects of tedious human manipulation can be avoided. Installation and execution on Windows, macOS, and Linux platforms have been tested, and the code has been developed to support access through Jupyter Notebooks, the command line, and job submission (e.g., Slurm) scripts. Examples of pre-configured workflows are available in various formats, and hands-on video tutorials illustrate their use.

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来源期刊
Wiley Interdisciplinary Reviews: Computational Molecular Science
Wiley Interdisciplinary Reviews: Computational Molecular Science CHEMISTRY, MULTIDISCIPLINARY-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
28.90
自引率
1.80%
发文量
52
审稿时长
6-12 weeks
期刊介绍: Computational molecular sciences harness the power of rigorous chemical and physical theories, employing computer-based modeling, specialized hardware, software development, algorithm design, and database management to explore and illuminate every facet of molecular sciences. These interdisciplinary approaches form a bridge between chemistry, biology, and materials sciences, establishing connections with adjacent application-driven fields in both chemistry and biology. WIREs Computational Molecular Science stands as a platform to comprehensively review and spotlight research from these dynamic and interconnected fields.
期刊最新文献
Issue Information Embedded Many-Body Green's Function Methods for Electronic Excitations in Complex Molecular Systems ROBERT: Bridging the Gap Between Machine Learning and Chemistry Advanced quantum and semiclassical methods for simulating photoinduced molecular dynamics and spectroscopy Computational design of energy-related materials: From first-principles calculations to machine learning
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