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porE : A code for deterministic and systematic analyses of porosities
Author(s) -
Trepte Kai,
Schwalbe Sebastian
Publication year - 2021
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.26484
Subject(s) - python (programming language) , benchmark (surveying) , computer science , porosity , fortran , implementation , workflow , void (composites) , code (set theory) , algorithm , set (abstract data type) , computational science , materials science , programming language , geology , database , composite material , geodesy
Accurate numerical calculations of porosities and related properties are of importance when analyzing metal–organic frameworks (MOFs). We present porE , an open‐source, general‐purpose implementation to compute such properties and discuss all results regarding their sensitivity to numerical parameters. Our code combines the numerical efficiency of Fortran with the user‐friendliness of Python . Three different approaches to calculate porosities are implemented in porE , and their advantages and drawbacks are discussed. In contrast to commonly used implementations, our approaches are entirely deterministic and do not require any stochastic averaging. In addition to the calculation of porosities, porE can calculate pore size distributions and offers the possibility to analyze pore windows. The underlying approaches are outlined, and pore windows are discussed concerning their impact on the analyzed porosities. Comparisons with reference values aim for a clear differentiation between void and accessible porosities, which we provide for a small benchmark set consisting of eight MOFs. In addition, our approaches are used for a bigger benchmark set containing 370 MOFs, where we determine linear relationships within our approaches as well as to reference values. We show how these relationships can be used to derive corrections to a give porosity approach, minimizing its mean error. As a highlight we show how complex workflows can be designed with a few lines of Python code using porE .