Machine learning meets quantum mechanics in catalysis - Lewis, James P.; Ren, Pengju; Wen, Xiaodong; Li, Yongwang; Chen, Guanhua (Frontiers in Quantum Science and Technology, 2023)
Over the past decade many researchers have applied machine learning algorithms with computational chemistry and materials science tools to explore properties of catalysts. There is a rapid increase in publications demonstrating the use of machine learning for rational catalyst design. In our perspective, targeted tools for rational catalyst design will continue to make significant contributions. However, the community should focus on developing high-throughput simulation tools that utilize molecular dynamics capabilities for thorough exploration of the complex potential energy surfaces that exist, particularly in heterogeneous catalysis. Catalystspecific databases should be developed to contain enough data to represent the complex multidimensional space that defines structure-function relationships. Machine learning tools will continue to impact rational catalyst design; however, we believe that more sophisticated pattern recognition algorithms would yield better understanding of structure-function relationships for heterogeneous catalysis.