Feature engineering methods for machine learning in heterogeneous catalysis.

Area of Science:

• Catalysis
• Materials Science
• Computational Chemistry

Background:

• Machine learning (ML) is increasingly vital in heterogeneous catalysis.
• Feature engineering is crucial for linking catalyst structure to ML models.
• This review examines the historical development and future of feature engineering in this field.

Purpose of the Study:

• To systematically review the evolution of feature engineering in heterogeneous catalysis.
• To identify current challenges and emerging strategies in feature engineering.
• To guide future innovations in ML-driven catalysis research.

Main Methods:

• Review of literature on feature engineering techniques in heterogeneous catalysis.
• Categorization of features: hand-crafted descriptors, symbolic regression, graph-based, topological, and multimodal.
• Analysis of challenges and proposed solutions.

Main Results:

• Feature engineering has progressed from simple descriptors to sophisticated graph and topological features.
• Recent advancements include multimodal representations integrating text and structure.
• Key challenges include underdeveloped multimodal approaches, interpretability, and cross-scale descriptors.

Conclusions:

• Feature engineering is central to ML in heterogeneous catalysis, enabling materials discovery and mechanistic insights.
• Addressing current challenges will further enhance ML's impact on catalysis.
• Continued innovation in feature engineering is essential for advancing heterogeneous catalysis research.