Structural chemistry of amorphous metal-organic frameworks from machine-learning-driven modelling

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This project will focus on developing an integrated computational and experimental approach to characterise and understand the microscopic structure of amorphous metal-organic frameworks (MOFs). The computational work will be built on machine learning (ML) potential models to obtain quantum-accurate simulations at orders of magnitude lower cost. The work will be complemented by experiments to validate the computational predictions, and later, to synthesise and characterise target amorphous MOFs (aMOFs). MOFs are a widely studied materials class, of interest in both academic research and industry. Owing to their porosity and relative ease of functionalisation, MOFs are excellent for many applications, including catalysis, gas adsorption and storage, and gas separation. aMOFs lack the long-range order of their crystalline counterparts, but are of interest owing to their promising application potential. The difficulty in elucidating the structural characteristics of amorphous compounds remains a major barrier in the field, and hinders the use of the materials in practice. General-purpose ML-based force fields are beginning to be developed for simple systems, achieving good material property and structure predictions at relatively low costs. However, ML force fields are difficult to develop for complex materials. New, carefully designed and validated methodologies are therefore required. This project will build upon previous research carried out in the group, in which geometric diversity expressed by complex materials was investigated quantitatively, using methods based on structural simplification. We are aiming to synthesise new, tailor-made aMOFs, as guided by computational predictions. Interatomic potential models developed during the project will be made available for use in the wider research community. Solving the structures of amorphous solids is challenging, and even more so for complex hybrid organic/inorganic solids. This project will develop a new approach to tackling this challenge, to further the atomic-scale understanding of these materials. This project falls within the EPSRC "Computational and theoretical chemistry" and "Functional ceramics and inorganics" research areas, and is primarily within the remit of the "Physical sciences" theme. The project aims align with EPSRC's stated strategy for this theme of meeting "societal and economic challenges that rely on fundamental science for solutions" by improving our understanding of an emerging class of functional materials relevant to practical applications. Regarding the first research area, the project is clearly of a fundamental nature: it will lead to the development and dissemination of new computational methodology with which to represent complex atomistic structures and their evolution over time. Regarding the second, perhaps more application-oriented research area, the project is of a "fundamental and high-risk" nature in line with EPSRC strategy: it will develop a deepened understanding of a class of functional materials that are only now beginning to be synthesised and studied, but hold substantial promise for applications, because of the analogy to their widely-studied crystalline counterparts.