POMSimulator: An open-source tool for predicting the aqueous speciation and self–assembly mechanisms of polyoxometalates

IF 3.4 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Enric Petrus, Jordi Buils, Diego Garay-Ruiz, Mireia Segado-Centellas, Carles Bo
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Abstract

Elucidating the speciation (in terms of concentration versus pH) and understanding the formation mechanisms of polyoxometalates remains a significant challenge, both in experimental and computational domains. POMSimulator is a new methodology that tackles this problem from a purely computational perspective. The methodology uses results from quantum mechanics based methods to automatically set up the chemical reaction network, and to build speciation models. As a result, it becomes possible to predict speciation and phase diagrams, as well as to derive new insights into the formation mechanisms of large molecular clusters. In this work we present the main features of the first open-source version of the software. Since the first report [Chem. Sci. 2020, 11, 8448-8456], POMSimulator has undergone several improvements to keep up with the growing challenges that were tackled. After four years of research, we recognize that the source code is sufficiently stable to share a polished and user-friendly version. The Python code, manual, examples, and install instructions can be found at https://github.com/petrusen/pomsimulator.

Abstract Image

POMSimulator:用于预测聚氧化金属酸盐的水相标示和自组装机制的开源工具。
无论是在实验领域还是在计算领域,阐明聚氧化金属酸盐的种类(浓度与 pH 值的关系)和了解其形成机制仍然是一项重大挑战。POMSimulator 是一种从纯计算角度解决这一问题的新方法。该方法利用基于量子力学方法的结果来自动建立化学反应网络,并建立物种模型。因此,它可以预测物种和相图,并对大分子团簇的形成机制提出新的见解。在这项工作中,我们介绍了该软件第一个开源版本的主要特点。自第一份报告[Chem. Sci. 2020, 11, 8448-8456]发布以来,POMSimulator 已经历了多次改进,以应对不断增长的挑战。经过四年的研究,我们认识到源代码已经足够稳定,可以分享一个完善且用户友好的版本。Python 代码、手册、示例和安装说明请访问 https://github.com/petrusen/pomsimulator。
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来源期刊
CiteScore
6.60
自引率
3.30%
发文量
247
审稿时长
1.7 months
期刊介绍: This distinguished journal publishes articles concerned with all aspects of computational chemistry: analytical, biological, inorganic, organic, physical, and materials. The Journal of Computational Chemistry presents original research, contemporary developments in theory and methodology, and state-of-the-art applications. Computational areas that are featured in the journal include ab initio and semiempirical quantum mechanics, density functional theory, molecular mechanics, molecular dynamics, statistical mechanics, cheminformatics, biomolecular structure prediction, molecular design, and bioinformatics.
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