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The document discusses the use of machine learning (ML) in catalysis, highlighting its ability to identify correlations in complex data and accelerate catalyst discovery. It introduces interpretable models that utilize spectroscopic descriptors to enhance understanding of catalytic processes and improve prediction accuracy. The proposed glass-box models generate analytical expressions and isolate physical contributions, making them robust even with limited data.

Author

prquezada94

Language

EN