and the speed of a less accurate method such as Voronoi sampling. The performance of our algorithm, including the two new features (sampling sphere scaling and rejection criterion) is illustrated in Figure , where we can see the improvement brought by each feature and how it compares to reference simulations. All CPU times are calculated using the smallest possible number of sampling points so that the respective algorithms reach convergence. With the implementation of a rejection condition, we find that surface sampling is even quicker than Voronoi sampling. Moreover, the increase of the size of the sampling sphere makes the surface sampling much more accurate, reaching an RMSE of 0.33 kJ mol -1 and an MAE of 0.21 kJ mol -1 . The ideal set of parameters, determined for porous materials from the CoRE MOF 2019 database, is (𝜆 = 1.6, 𝜇 = 0.85) in order to combine the lowest error and smallest computational cost. By combining both of these new features to the algorithm, we have a final surface sampling method with an RMSE of 0.33 kJ mol -1 and an average computation time of 0.34 s per structure. According to the data of the Table , it is about 6 times more accurate and 26% faster than Voronoi sampling, and it is also about 430 times faster than a Widom insertion with 12k cycles.