Explainable machine-learning predictions for catalysts in CO₂-assisted propane oxidative dehydrogenation

Abstract

Data-driven machine learning is a valuable perspective on light alkane conversion, which can advise on catalyst development.

Redefining the Stability of Water Oxidation Electrocatalysts: Insights from Materials Databases and Machine Learning

“…Thus, this approach should be viewed merely as a preliminary exercise for TM X-ides. While these ML approaches are robust and have been extensively utilized to derive significant insights into the catalytic performance of various materials, ,− researchers must exercise caution when making claims about the relevance and impact of specific descriptors. Future investigations must prioritize the use of more reliable data sets, as the quality of data fed into ML models critically determines their output’s accuracy. Simply put, the efficacy of ML outcomes is directly proportional to the quality of the input data.…”