Interpretable Machine Learning of Chemical Bonding at Solid Surfaces.

Area of Science:

• Materials Science
• Computational Chemistry
• Artificial Intelligence

Background:

• Understanding chemical bonding is key for designing novel catalytic materials.
• Machine learning (ML) accelerates materials discovery using experimental and simulation data.
• Complex ML models, like deep learning, often lack transparency, hindering scientific insight.

Purpose of the Study:

• To review recent advancements in interpretable ML for scientific applications.
• To highlight the importance of transparency in ML for understanding chemical properties.
• To discuss strategies for opening the 'black box' of complex ML algorithms.

Main Methods:

• Focus on feature engineering for interpretable ML.
• Discuss algorithm development for enhanced transparency.
• Explore post hoc analysis techniques for ML model explanation.

Main Results:

• Interpretable ML offers insights into chemical bonding and reactivity.
• Advances in feature engineering and algorithms improve ML model transparency.
• Post hoc analyses provide explanations for complex model predictions.

Conclusions:

• Interpretability is foundational for next-generation ML and AI in science.
• Transparent ML models will drive innovation in materials science and catalysis.
• Interpretable ML facilitates deeper understanding and accelerates scientific discovery.