Before revision:  The physical interpretability also makes the CRNN not only capable of fitting the data for a given system but also developing knowledge of unknown pathways that could be generalized to similar chemical systems . Finally, the approach is applied to several chemical systems in chemical engineering and biochemistry to demonstrate its robustness and generality .
After revision:  The physical interpretability also makes the CRNN not only capable of fitting the data for a given system but also developing knowledge of unknown pathways that could be generalized to similar chemical systems .