In the present article, we described a novel algorithm for the high-speed calculation of adsorption enthalpy in nanoporous materials, that takes a unique approach to reduce the sampling necessary. This new algorithm is based on the core principle of dimensional reduction, from a volume problem to a surface one. The algorithm is proven to be significantly faster than the reference Widom insertion (random sampling of porous space). Moreover, the error associated is found to be of the order of 0. Based on these results, this algorithm has important potential for applications in the current computational analysis workflows of material databases, such as high-throughput screening studies. For instance, this algorithm can be used to get a fast approximation of the low-loading adsorption enthalpy of a molecule inside nanoporous materials. This cheap evaluation of the enthalpy can be used to screen out the structures with little affinity with the targeted adsorbate molecule. It can also be used as a thermodynamic descriptor for selectivity prediction in a machine learning model, as done by Simon et al. The computational speedup brought by this novel methodology can also enable the screening of materials databases at larger scale in the future.