Periodic Reporting for period 4 - MaGic (The Materials Genome in Action)
Okres sprawozdawczy: 2020-05-01 do 2021-04-30
It is now possible to make an enormous spectrum of different, novel nanoporous materials simply by changing the building blocks in the synthesis of Metal Organic Frameworks (MOF) or related materials. This unique chemical tunability allows us to tailor-make materials that are optimal for a given application. The promise of finding just the right material seems remote however: because of practical limitations we can only ever synthesize, characterize, and test a tiny fraction of all possible materials. To take full advantage of this development, therefore, MaGic has developed a set of computational methods collectively referred to as Materials Genomics, to rapidly screen large numbers of materials and obtain fundamental insights into the chemical nature of the ideal material for a given application.
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.
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.
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/
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/ 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.
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 [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/ 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 significant achievement was to demonstrate that we can design a Metal Organic Framework using computational method that can separate CO2 from wet flue gasses. This is a significant challenge as adsorption site in material that attract CO2 are often even more attractive for water. Hence, materials that are excellent for dry flue gasses typically perform much worse at wet conditions. The approached we developed was based on drug design; identifying the common feature of the best performing materials, and we were in the unique position that in the team we also had the expertise to synthesize a material with these characteristics. Subsequent testing showed it outperformed the commercial materials in the capacity to capture CO2 for wet flue gasses. That this work was published in Nature shows that this achievement is really beyond the state of the art. The other important aspect of this work is that it combined all the efforts of MaGic (see P. G. Boyd, et al, Data-driven design of metal-organic frameworks for wet flue gas CO2 capture Nature 576 (7786), 253 (2019) http://dx.doi.org/10.1038/s41586-019-1798-7).
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).
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).