Table 1.

Summary of recent studies that use machine learning to predict H₂ adsorption in MOFs

Study	ML features	ML method	Properties predicted	Accuracy
Anderson et al.43	epsilon, temperature, pressure, ρcrys, vf, vsa, mpd, lcd, alchemical catecholate site density, unit cell volume	neural network76	total volumetric H2 for pressures 0.1, 1, 5, 35, 65, and 100 bar at 77, 160, and 295 K	AUE = 0.75–2.93 g-H2 L−1
Bucior et al.80	energetics of MOF-guest interactions	multilinear regression with LASSO76	deliverable H2 storage capacity between 2 and 100 bar at 77 K	R2 = 0.96; AUE = 1.4–3.4 g-H2 L−1; RMSE = 3.1–4.4 g-H2 L−1
Borboudakis et al.63	92 binary features based on linker, metal cluster, and 12 functional groups	ridge linear regression and support vector machine with polynomial/Gaussian kernel76, 77, 78	total H2 storage capacity at 1 bar and 77 K	AUE = 0.47 (ridge regression), 0.50 (SVM) g-H2 g−1-MOF
Thornton et al.61	adsorption energy, ρcrys, vf, gsa, vsa, lcd	neural network76	net H2 capacity for pressure swing between 1 and 100 bar at 77 and 298 K	R2 = 0.88; RMSE = 3.6 g-H2 L−1

ρ_crys, vf, vsa, mpd, lcd represent single-crystal density, void fraction, volumetric surface area, maximum pore diameter, and largest cavity diameter, respectively. R², AUE, and RMSE represent the coefficient of determination, average unsigned error, and root-mean-square error, respectively.