Bayesian optimisation has been widely employed to optimise the discovery of new materials in computational screenings. Nanoporous materials are developing technologies used to store, capture and sense gases. Given a target molecule, a typical task is to search a large library of materials to find the one with optimal adsorption properties. However, the current cost of such searches, either experimentally or computationally, is relatively high and limits the number of systems that can be studied. Deshwal et al. applied Bayesian optimisation to search a database of ∼70,000 hypothetical covalent organic frameworks (COFs) for the system with the highest simulated methane deliverable capacity. Their Bayesian optimisation approach identified ∼30 % of the top 100 ranked COFs after evaluating only 140 systems, including identifying the optimal material in the dataset. Seko et al. used Bayesian optimisation with the Kriging method 66 to discover materials with low lattice thermal conductivity (κ l ) for thermoelectric applications. They explored over 50,000 potential materials using an initial dataset of 101 first-principles anharmonic lattice dynamics calculations. Their study identified 21 materials with low lattice thermal conductivities suitable for high-performance thermoelectrics, including two compounds with κ l less than 0.5 W m -1 K at 300 K and a narrow electronic band gap less than 1 eV.