Periodic Reporting for period 4 - MaGic (The Materials Genome in Action)
Reporting period: 2020-05-01 to 2021-04-30
These computational methods involve the generation of libraries with large number of hypothetical MOFs; these libraries contain the crystal structure of materials that could potentially be synthesized. The second component is the development of molecular simulation techniques that can sufficiently accurate and efficient predict the properties of these MOFs on the basis of the crystal structure, and the final component are workflows to screen thousands of materials for applications related to gas separations and gas storage.
The computational methods were used to address one of the biggest challenges of our generation: reducing CO2 emission through the development of better materials for carbon capture. The most challenging separation is to capturing CO2 in wet flue gasses. MOFs that are optimized for the separation of CO2 from nitrogen do not perform well when using realistic flue gas that contains water, because water competes with CO2 for the same adsorption sites and thereby causes the materials to lose their selectivity. The MaGic team carried out a data mining of a computational screening library of over 300,000 MOFs to identify different classes of strong CO2- binding sites—which we term ‘adsorbaphores’—that endow MOFs with CO2/N2 selectivity that persists in wet flue gases. We subsequently synthesized two water- stable MOFs containing the most hydrophobic adsorbaphore, and found that their carbon-capture performance is not affected by water and outperforms that of some commercial materials.
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/
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 . 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/ and AiiDAlab https://www.materialscloud.org/work/aiidalab . In the Materials Cloud all structures that we studied can be accessed. AiiDAlab provides easy access to some of the programs we have developed.
The most unexpected result is the work we called “Capturing Chemical Intuition” For capturing CO2 we were getting involved in the synthesis and we wanted to make some progress there. We observed that our students were getting better in making MOFs but they could not tell us why. We then realized that we can apply machine learning technique to analyze the failed and partly successful experiments. And from this we could quantify the chemical intuition, and show how it can be used to speed up the synthesis of other materials. The interesting part here is that failed experiments are not routinely measured, our work shows that it could completely change chemistry if these results would be available (see S. M. Moosavi, et al, Capturing chemical intuition in synthesis of metal-organic frameworks Nat. Commun. 10 (1), 539 (2019) http://dx.doi.org/10.1038/s41467-019-08483-9).