Mechanism-Guided Catalyst Discovery for Methane C-H Activation via Structure-Aware Multisource Transfer Learning.

In complex heterogeneous systems, data-driven catalyst discovery is severely hindered by the scarcity of kinetic data and the breakdown of traditional linear scaling relationships caused by the diverse local coordination environments. Herein, we formulate a mechanism-driven approach to alleviate data dependence and develop a multisource transfer learning (MS-TL) framework that leverages the knowledge embedded in abundant adsorption data sets while accurately capturing local structural dependence. Taking methane C-H activation as a representative case, this framework extracts key thermodynamic descriptors corresponding to the initial, transition, and final states as source domains, enabling a deep fusion of multidimensional thermodynamic knowledge while preserving local structural information. Using this framework, we achieved universal predictions of barriers across various facets and compositions in complex alloys. Subsequent data-driven analysis recovers the classical Sabatier principle beyond the limits of linear scaling, revealing a multidimensional volcano-shaped trend that delineates the optimal catalytic window. Furthermore, we propose a temperature-barrier composite kinetic descriptor that quantitatively bridges microscopic theoretical calculations with macroscopic experimental methane oxidation rates, establishing a new data-driven paradigm for rational catalyst design under realistic operating conditions.