Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution

Abstract

Finding the best material for a specific application is the ultimate goal of materials discovery. However, there is also the reverse problem: when experimental groups discover a new material, they would like to know all the possible applications this material would be promising for. Computational modeling can aim to fulfill this expectation, thanks to the sustained growth of computing power and the collective engagement of the scientific community in developing more efficient and accurate workflows for predicting materials' performances. We discuss the impact that reproducibility and automation of the modeling protocols have on the field of gas adsorption in nanoporous crystals. We envision a platform that combines these tools and enables effective matching between promising materials and industrial applications.

Figure 1

(a) Papers mentioning “Zeolite”, “Metal Organic Framework”, and “Covalent Organic Framework” in the title or the abstract, as parsed from Scopus in July 2020. The right column collects histograms for the deposition of materials in publicly available databases. (b) Zeolite code types by year of assignment, from the database of the International Zeolite Association (IZA). (c) MOF-subset of the Cambridge Structural Database (CSD, May 20 update) by year of publication (orange). MOFs in the CoRE-2019 “All solvent Removed” (ASR) subset (purple) are selected from the CSD release of November 2017 with criteria such as three-dimensionality of the framework and permeability to small molecules. (d) COFs in the CURATED-COFs database (June 20 update), by year of publication.^,

Figure 2

Scheme of exemplary workflow. The user starts by uploading the atomic structure of a crystalline materials in the CIF format, which triggers the refinement of the atomic positions, the computation of pore geometry, and thermodynamic and transport properties. Finally, its performance for specific applications is evaluated, and the material is ranked versus other candidates.

Figure 3

Performance of COF structures for CO₂ capture: parasitic energy required for the process versus gravimetric working capacity. Markers of the 250 new COFs are color-coded based on their ranking from high performance (low parasitic energy and high working capacity, green) to low performance (red). Markers of materials already included in ref (11) are shown in light gray.

Figure 4

Performance of CURATED-COFs for H₂ storage at (a) cryogenic and (b) near-ambient conditions, (c) methane storage, (d) oxygen storage, (e) Xe/Kr separation, and (f) (H₂S)/water separation. The ranking is color-coded from high performance (green) to low performance (red). Selectivities are computed as the ratio of the Henry coefficients of the two gases at 300 K. The coordinates of the markers for T-COF-2 and JUC-509 are highlighted by dashed and solid lines, respectively.

Figure 5

AiiDA provenance graph of the workflow tracing the entire path from the initial CIF file to the properties and performance computed for it. The graph shows process and data as nodes, and their connection: in an interactive visualization, each node can be browsed to explore the input parameters of the calculation, its output results, and the details of the processes. Colors distinguish different modules of the workflow, whose source code is available online. The modules make use of other popular open-source tools, such as CP2K, Raspa, Zeo++, and chargemol.

Figure 6

Crystal structures of (a) T-COF-2 and (b) JUC-509. Elements: H (white), C (gray), N (blue), oxygen (red), S (yellow), Cl (green).