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Tracking functional materials by high-throughput screening and machine learning - David Fairen-Jimenez

Overcoming the environmental, economic and energetic problems related to multi-billion gas storage and separation applications would lead to great global benefits (Scholl, NATURE, 2016, 532, 435-437). This requires the development of new nanoporous materials serving as catalysts, energy storage media and sorbents, for which novel metal-organic frameworks (MOFs) arise as promising candidates. Their large chemical diversity of MOFs has resulted in more than 85,000 structures synthesised in the last 20 years (P. Z. Moghadam, CHEM. MATER, 2017, 29,2618-2625). Our aim is to find suitable materials, among the thousands available, for hydrogen storage, CO2 capture and for challenging hydrocarbon separations. We will develop and use high-throughput computational techniques to screen the structural and geometrical features of MOFs and their performance towards these applications (Colon, CHEM. SOC. REV. 2014, 43, 5735-5749; Moghadam, NAT. COMMUN., 2018, 9, 1378) using advanced visualisation in data mining and machine learning. This will allow us to i) identify functional materials for critical applications, ii) rationalise the relationship between MOFs structural features and performance, and iii) amplify and speed up the predictive capacity of molecular simulations, by implementing new methods for data visualisation and developing machine learning to exploit the scientific data available.