Optimization of Cu-based Oxide Catalyst for Methanol Synthesis by the Activity Map Envelope Derived from a Neural Network

摘要

The combinatorial approach is a successful tool for material development and for heterogeneous catalyst development.Combinatorial tools were developed consisting of a high-throughput screening reactor using a 96-well microplate,activity mpping by a neural network and optimization by a genetic algorithm.The tools were designed and manufactured to optimize Cu-based oxide catalyst for methanol synthesis.Escape from local optima in the search space is easy by GA,but the efficiency of search is not so high.Instead of GA,a more straight-forward method was applied:all 230,000 activities of all possible combinations of catalyst components with 5% resolution were predicted by a neural network.These activities were visualized by mapping using two parameters,such as Cu and Zn composition,to find the global optimum.