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The Materials Genome in Action

Periodic Reporting for period 3 - MaGic (The Materials Genome in Action)

Reporting period: 2018-11-01 to 2020-04-30

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, we need to develop alternative techniques, 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.

Our ERC team tackles the challenge and promise posed by this unprecedented chemical tunability through the development of a multi-scale computational approach, which aims to reliably predict the performance of novel materials before synthesis. The team is developing methodologies to generate libraries of representative sets of synthesizable hypothetical materials and perform large-scale screening of these libraries. These studies should give us fundamental insights into the common molecular features of the top-performing materials. The methods developed will be combined into an open access infrastructure in which our hypothetical materials are publicly accessible for data mining and big-data analysis.

The project is organized in three Work Packages:

(1) Screen materials for gas separations and develop the tools to predict the best materials for carbon capture. In this Work Package the objective is to develop different computational tools to efficiently generate materials in silico and predict their properties.

(2) Gain insights into and develop a computational methodology for screening the mechanical properties of nanoporous materials. Often MOFs are not sufficiently stable against mechanical forces. The objective of this work package is to develop the tools to efficiently compute the mechanical properties and to use these tools to obtain a molecular understanding of what makes a MOF mechanically stable.

(3) Achieve a detailed molecular understanding of the performance of some MOFs. The objective of this work package is to obtain a detailed molecular understanding of the behaviour of MOFs that cannot be explained with the conventional methods

The overall objective is to combine the methods and expertise developed in these work packages to predict novel materials to efficiently capture carbon. The ultimate aim is to find materials that can capture CO2 from flue gasses more efficiently, and hence reduce the costs of carbon capture.
We have focused on developing methodologies to predict in silico different materials. We have generated a library of MOFs with the MOF-74 topology. These materials are of practical interest because of they contain open metal sites. Of particular importance was that subsequently one of the predicted structures was successfully synthesized and the predicted adsorption isotherms of this material were in excellent agreement with the experimental one. We also generated a library of covalent organic frameworks (COFS), a library of schwarzites as can be obtained from zeolite templating, and a library of 2-D zeolite materials. In the future these materials will be screened for different applications (methane storage and different gas separations).

The method developments were focused on how to quantify similarity between pore shapes. At present the most reliable method is inspection by eye, but with thousands of materials this is not feasible anymore, we develop a methodology using applied topology to quantify the similarity between pores. A surprising problem in the databases of MOF crystal structures is that there are many duplicates, which the conventional techniques are not able to detect. We developed a procedure to detect these duplicates. We also have developed an improved algorithm to compute the pore volume of these materials. For some particular systems the conventional method gave incorrect results.

In addition, significant progress has been made on understanding the mechanical properties of MOFs. As a first step we tested the accuracy of force fields to predict mechanical properties. Once we established that these force fields were sufficiently accurate, we used them in a systematic study on how the MOF topology and functionalization contribute to the mechanical stability of the materials. This work gave us a simple recipe to predict whether functionalization of the MOF would lead to a stronger material. We also made significant progress in developing Machine Learning tools to analyse the data.

One of the highlight was a computational discovery of a novel material to capture CO2. This discovery was possible my development of a novel algorithm to screen materials inspired by drug design. Of particular importance was the prediction that the material also performed well in wet flue gasses. Subsequent synthesis and testing of this material showed that it outperformed commercial materials. The results of this work was published in Nature.
A mayor breakthrough was the discovery of a novel class of metal organic frameworks that are able to efficiently capture CO2 from wet flue gasses. We expect to further optimise the separation. In addition, we are now developing machine learning tools to more efficiently screen the databases.
Topological diversity of the top performing zeolites for methane storage