Training algorithms.
A novel training algorithm based on "Self-Consistency" (SC) replaced the standard procedure of linearly increasing of the TS (100,200,300, ...).
Our SC-ML methodology was tested in 5.000 experimentally made MOFs for investigating the storage of various gases (H2, CH4, CO2, H2S, H2O).
For all gases examined, the use of both descriptors instead of building blocks leads to significantly more accurate predictions, while the
number of MOFs needed for the training of the ML algorithm in order to achieve a specified accuracy can be reduced by an order of magnitude. References
[1] "Fast Screening of Large Databases for Top Performing Nanomaterials Using a Self-Consistent, Machine Learning Based Approach", George S Fanourgakis, Konstantinos Gkagkas, Emmanuel Tylianakis, George E Froudakis, The Journal of Physical Chemistry C 124, 19639 (2020)