As next-generation materials for applications in the chemical industry, energy conversion, and more become increasingly complex to meet the stringent requirements, so does their atomic-level structure. This complexity makes it very challenging to exactly identify the structure that has been synthesized experimentally, especially since scientists rarely have an overview of all possible models. New research from the Center for Molecular Modeling (CMM) at Ghent University levels the playing field by constructing all possible models and ranking these different possible models using quantum mechanical calculations, until only a single model remains standing before making our first guess in this convoluted game of “Guess Who?”.
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Imagine yourself as the newest supervisor of a building company focused on K’NEX construction... at the nanoscale. Your predecessor succeeded in making a very intricate K’NEX design and subsequently left the company. Your job is to capitalize on this invention by providing step-by-step guidelines of how the different K’NEX building blocks come together to form this promising material and maybe even come up with similar – even more successful – designs. However, your predecessor left no design instructions at the construction site. The only thing you can base yourself on is a fuzzy photograph of his successful design and the knowledge that K’NEX structures are modular, where individual blocks are combined into a larger structure, leading to an enormous number of possible combinations even with only a few types of blocks. How to find out in which way the different building blocks need to be combined to achieve that revolutionary K’NEX design?
Although fictional, this situation is strangely relatable for material scientists that investigate the construction of modular materials. In these modular materials, rigid building blocks are assembled into periodic patterns on the nanoscale. These materials are incredibly versatile and the structures are very appealing to solve various technological challenges, as appropriate building blocks can be selected for the application at hand, such as energy storage, shock absorption, greenhouse gas capture, or catalysis. However, the sheer number of possible ways in which the building blocks can be combined often makes their characterization arduous. Fortunately, researchers at the Center for Molecular Modeling (CMM) at Ghent University have proposed a protocol to accept this challenge, which is now published in Angewandte Chemie.
Shedding light on the materials’ structure... at the nanoscale
Usually, to describe the atomic-level structure of such next-generation building block materials, scientists start by looking at how the material behaves, and then try to make guesses about its structure. This guessing is often ambiguous and can induce strong biases towards earlier models. In contrast, the new approach completely reverses this workflow. The newly published methodology starts by combining the different building blocks in all different ways, constructing all possible structural models in a computer. This removes the ambiguity in making an initial guess, leading to an easy-to-use, systematic, and reliable workflow. Subsequently, all the models are subjected to quantum mechanical calculations to accurately consider the atoms’ behaviour at the nanoscale, allowing for a comparison between theory and experiment which facilitates a quantitative ranking between the possible models.
The building blocks that make up these materials have sizes of only a few nanometres. Therefore, looking into their structure requires specific techniques. In X-ray diffraction, X-rays with a specific wavelength are focused on the experimental sample and reflected on the particles inside the structure. For a perfectly periodic material, these reflected rays strongly interfere and give rise to typical diffraction patterns. These diffraction patterns directly relate to the atomic content and periodic patterns within the material, and thus allow for the identification of the structure, at least, for ideal materials. Therefore, material scientists often report these X-ray diffraction patterns when new materials are made, without detailed information about the atomic-level structure of these materials.
However, real-life materials such as covalent organic frameworks (COFs) are not always perfectly periodic: they contain defects and can be very dynamic. This often leads to smeared out diffraction patterns, which makes it very ambiguous to assign a structural model to a given diffraction pattern. In the new approach, this ambiguity is strongly reduced by considering how well each possible structural model matches with the experimental diffraction pattern, thereby ranking the different models and highlighting the most probable one.
The imitation game: mimicking the experimental conditions
One of the most challenging aspects of this approach is to mimic the experiment as closely as possible within a computer simulation. At room temperature, for instance, atoms inside the experimental sample tend to be much more dynamic than at colder temperatures and move inside the material over time. These dynamics need to be accounted for, also in the quantum mechanical calculations used to rank the different hypothetical models. In the newly published procedure, this dynamics is correctly accounted for, resulting in unprecedented agreements between experimental and theoretical XRD patterns and giving confidence that the atomic-level structure of these materials is correctly retrieved.
These results offer the perspective to resolve the atomic structure of complex materials and understand what makes their design so revolutionary. By using this knowledge, new design principles can be established to build next-generation materials with even better performance, ushering in a new era in material design.
These results were published in Angewandte Chemie for the MOF2020WEB special collection:
- Quantifying the likelihood of structural models through a dynamically enhanced powder X-ray diffraction protocol.
- Sander Borgmans, Sven M. J. Rogge, Juul S. De Vos, Christian V. Stevens, Pascal Van Der Voort, Veronique Van Speybroeck.
- Angewandte Chemie, http://doi.org/10.1002/anie.202017153
- ir. Sander Borgmans, dr. ir. Sven M. J. Rogge, ir. Juul S. De Vos, prof. dr. ir. Veronique Van Speybroeck
- Center for Molecular Modeling
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