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and could offer more nearly optimal properties for a given application or process. Carbon capture research could also greatly benefit from a “reverse engineering” approach through unorthodox or novel applications of existing or new computational tools, which could be used to discover materials that closely match a desired set of properties. Computation-based discovery has the potential advantage of being able to find optimal materials among known classes (e.g., the best amine for liquid capture, or the best MOF for solid adsorption). This approach could also discover novel materials not related to any currently known chemical systems. BASIC SCIENCE CHALLENGES AND OPPORTUNITIES The problem of identifying the best molecule or material for a given application is common across areas of chemistry. The most advanced approaches to molecular discovery are in use in the drug discovery field, where they are known as “cheminformatics.” Cheminformatics is focused on storing and processing data representing large sets of chemical structures. Given the success and wide implementation of cheminformatics techniques in the pharmaceutical industry, it is intriguing to ask whether similar approaches could be developed to discover unimagined systems of materials and/or processing techniques particularly suitable for carbon capture. Two different approaches for the in silico discovery of new materials can be envisioned: virtual screening and inverse design. Virtual screening starts with a database of possible structures, which are then prioritized according to an assumed/estimated measure of the extent to which they meet set points of desired properties. Inverse design, in contrast, requires knowledge of some or all of the relationships between properties and structure. This knowledge base is then used to directly predict structures with a given (desired) property. Discovery by Screening The critical part of discovery by virtual screening is the availability of structural databases. For example, commercially available databases (or databases of commercially available compounds) can be screened to identify new amines to scrub CO2. Extending these databases to materials that have not yet been synthesized would allow us to explore the limits of maximum achievable performance. In drug design, generation of virtual libraries of drug candidates is relatively straightforward, as it requires merging organic fragments to produce relatively small molecules. Similar search strategies for inorganic materials, in contrast, lead to a combinatorial explosion of a number of structures that would have little chemical relevance. Developing design strategies for databases of hypothetical materials that have a reasonable chance of being synthesized remains a key challenge.4 The generation of a database of hypothetical materials is the first step to discovery of novel materials. Another anticipated challenge involves the screening of such databases to identify structures with closer to optimal performance. Given the very large number of pore structures, these screening strategies must rely on algorithms that can characterize the pore topologies (Figure 33). Molecular simulation techniques are too time consuming to screen the many million structures and screening strategies needed to find the optimal material. 109PDF Image | 2020 Carbon Capture
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