MaGic main activity was the development and application of the methodologies to computationally discover novel materials for Carbon Capture. In MaGic we focused on the use of nanoporous materials to capture CO2, in particular we considered Metal Organic Frameworks (MOFs) and Covalent Organic Frameworks (COFs). By combining metal nodes and organic linkers one can make millions of different MOFs, or COFs by combining linkers. The central theme of MaGic was to identify the best performing material before they are synthesized. This involves the generation of libraries, the prediction of properties, and the screening of libraries. As we did find some interesting materials, we extended the scope of MaGic to synthesis and testing. Also, as we collected large amount of data, we also developed data-science methods to analyze the data. As Open Science was an important part of MaGic we also contributed to the creation of an open science infrastructure for computational materials design. Finally, many of the methodologies we have developed in MaGic can be used in applications not directly related to MaGic. These applications nicely illustrate the impact of MaGic beyond the field of gas separation and storage.
In the literature several libraries have been published. We extended these libraries to some classes of materials that were missing and enlarged them. The structure of these libraries is accessible through the materials cloud:
https://www.materialscloud.org/(odnośnik otworzy się w nowym oknie) The predictions of properties; included methods to compute the mechanical strength, thermoelasticity, diffusion coefficients, assign charges, etc. These methods are subsequently used for our screening studies, the developed codes can all publicly available.
We screened the libraries of materials for hydrogen storage, methane storage, and carbon capture. These screening studies gave interesting leads for materials to be synthesized.
We synthesized and tested the most promising materials for carbon capture [39]. These materials were characterized and tested for their performance in wet flue gasses. We showed that these materials outperformed the commercial ones in terms of their capture capacity. One of the observations in the MOF synthesis is that it is a lot of trial and error to find the right conditions in which a MOF crystal forms. We developed the computational methodologies to speed-up the screening of these conditions. In addition, as the experiments have over 200 failed and partly successful conditions, we developed a machine learning approach to learn from these failed experiments.
Important in MaGic was that the data and programs are Open Access, for this MaGic contributed to the development of an Open Science infrastructure, which includes the Materials Cloud :
https://www.materialscloud.org/(odnośnik otworzy się w nowym oknie) and AiiDAlab
https://www.materialscloud.org/work/aiidalab(odnośnik otworzy się w nowym oknie) . In the Materials Cloud all structures that we studied can be accessed. AiiDAlab provides easy access to some of the programs we have developed.
