The application of machine learning for predicting the methane uptake and working capacity of MOFs

Multiple linear regression analysis, as a part of machine learning, is employed to develop equations for the quick and accurate prediction of the methane uptake and working capacity of metal-organic frameworks (MOFs). Only three crystal characteristics of MOFs (geometric descriptors) are employed for developing the equations: surface area, pore volume and density of the crystal structure. The values of the geometric descriptors can be obtained much more cheaply in terms of time and other resources compared to running calculations of gas sorption or performing experimental work. Within this work sets of equations are provided for the different cases studied: a series of MOFs with NbO topology, a set of benchmark MOFs with outstanding methane storage and working capacities, and the whole CoRE MOF database (11 000 structures). Multiple linear regression as a part of machine learning is employed to develop equations to predict the methane uptake and working capacity of MOFs. Only three geometrical descriptors are used in the equations: surface area, pore volume and density.