Soft computing techniques applied to combinatorial catalysis: A new approach for the discovery and optimization of catalytic materials

A soft computing technique based on the combination of Artificial Neural Networks (ANNs) and a Genetic Algorithm (GA) has been developed for the discovery and optimization of new materials when exploring a high-dimensional space. This technique allows the experimental design in the search of new solid materials with high catalytic performance when exploring simultaneously a large number of variables such as elemental composition, manufacture procedure variables, etc. This novel integrated architecture allows one to strongly increase the convergence performance when compared with the performance of conventional GAs. It is described how both artificial intelligence techniques are built to work together. Moreover, the influence of algorithm configuration and the different algorithm parameters in the final optimization performance have been evaluated. The proposed optimization architecture has been validated using two hypothetical functions, based on the modeled behavior of multi-component catalysts explored in the field of combinatorial catalysis.