TY - JOUR
T1 - Materials Informatics with PoreBlazer v4.0 and the CSD MOF Database
AU - Sarkisov, Lev
AU - Bueno-Perez, Rocio
AU - Sutharson, Mythili
AU - Fairen-Jimenez, David
N1 - Funding Information:
L.S. would like to thank Prof. Shane Telfer of Massey University and his group for early tests of the code, Drs. Senja Barthel and Deniele Ongari for helpful comments and clarifications, and many scientists around the world who provided feedback and pointed out the bugs in the code. Computational work was supported by the Cambridge High-Performance Computing Service, the Cambridge Service for Data-Driven Discovery (CSD3). D.F.-J. thanks the Royal Society for funding through a University Research Fellowship and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (NanoMOFdeli), ERC-2016-COG 726380.
Publisher Copyright:
© 2020 American Chemical Society.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/11/27
Y1 - 2020/11/27
N2 - The development of computational methods to explore crystalline materials has received significant attention in the last decades. Different codes have been reported to help researchers to evaluate and learn about the structure of materials and to understand and predict their properties. Here, we present an updated version of PoreBlazer, an open-access, open-source Fortran 90 code to calculate structural properties of porous materials. The article describes the properties calculated by the code, their physical meaning and their relationship to the properties that can be measured experimentally. Here, we reflect on the methods in the code and discuss features of the most recent version. First, we demonstarte the capabilities of PoreBlazer on the prototypical metal-organic framework (MOF) materials, HKUST-1, IRMOF-1 and ZIF-8, and compare the results to those obtained with other codes, Zeo++ and RASPA. Second, we apply PoreBlazer to the recently assembled database of MOF materials-the CSD MOF subset-and compare properties such as accessible surface area and pore volume from PoreBlazer and the two other codes, and reflect on the possible sources of the differences. Finally, we use PoreBlazer to illustrate how correlations between various structural characteristics can be mined using interactive, dynamic data visualization and how material informatics approaches-including principal component analysis and machine learning-can accelerate the discovery of new materials and new functionalities. The results of these calculations, along with the PoreBlazer code, documentation, and case studies are available online from https://github.com/SarkisovGroup/PoreBlazer. The data visualization tool is available at https://aaml-explorer-geo-prop.herokuapp.com), and the principal component analysis is available at https://aaml-pca-geo-prop.herokuapp.com.
AB - The development of computational methods to explore crystalline materials has received significant attention in the last decades. Different codes have been reported to help researchers to evaluate and learn about the structure of materials and to understand and predict their properties. Here, we present an updated version of PoreBlazer, an open-access, open-source Fortran 90 code to calculate structural properties of porous materials. The article describes the properties calculated by the code, their physical meaning and their relationship to the properties that can be measured experimentally. Here, we reflect on the methods in the code and discuss features of the most recent version. First, we demonstarte the capabilities of PoreBlazer on the prototypical metal-organic framework (MOF) materials, HKUST-1, IRMOF-1 and ZIF-8, and compare the results to those obtained with other codes, Zeo++ and RASPA. Second, we apply PoreBlazer to the recently assembled database of MOF materials-the CSD MOF subset-and compare properties such as accessible surface area and pore volume from PoreBlazer and the two other codes, and reflect on the possible sources of the differences. Finally, we use PoreBlazer to illustrate how correlations between various structural characteristics can be mined using interactive, dynamic data visualization and how material informatics approaches-including principal component analysis and machine learning-can accelerate the discovery of new materials and new functionalities. The results of these calculations, along with the PoreBlazer code, documentation, and case studies are available online from https://github.com/SarkisovGroup/PoreBlazer. The data visualization tool is available at https://aaml-explorer-geo-prop.herokuapp.com), and the principal component analysis is available at https://aaml-pca-geo-prop.herokuapp.com.
UR - https://doi.org/10.1021/acs.chemmater.0c03575
U2 - 10.1021/acs.chemmater.0c03575
DO - 10.1021/acs.chemmater.0c03575
M3 - Article
SN - 0897-4756
JO - Chemistry of Materials
JF - Chemistry of Materials
ER -