Data-driven causal inference of process-structure relationships in nanocatalysis

While the field of nanocatalysis has benefited from the application of conventional machine learning methods by leveraging the correlations between processing/structure/ property variables, the outcomes from purely correlational studies lack actionability due to missing mechanistic insights. Statistical learning, particularly causal inference, can potentially provide access to more actionable insights by allowing the discovery and verification of deeply obscured causal relationships between variables, using strong correlations identified from interpretable machine learning models as starting points. Recent studies that exemplify the collaborative usage of correlational and causal analysis in catalysis are discussed, including studies potentially benefiting from this approach. Some challenges remaining in the application of inference techniques to the field are identified and suggestions of future directions are provided.

Automated summary

The field of nanocatalysis has benefited from traditional machine learning methods, but purely correlational studies lack actionability. By utilizing causal inference, deeply obscured causal relationships between variables can be discovered and verified, providing more actionable insights. Collaborative usage of correlational and causal analysis in catalysis has been discussed, as well as challenges and future directions in the application of inference techniques.