Material Discovery, Renewable Energy, and the Scope of Shallow Learning and Deep Learning

Material discovery for renewable energy applications has rapidly evolved through the integration of artificial intelligence (AI), particularly machine learning (ML) techniques. Shallow learning methods, such as support vector machines and random forests, have proven effective in analyzing structured datasets, enabling property prediction and material screening with limited data. However, the growing complexity of material structures and the need to capture nonlinear relationships have driven the adoption of deep learning (DL). Deep neural networks and graph-based models can automatically extract hierarchical features from high-dimensional data, facilitating the design of novel materials for solar cells, batteries, and catalysts. The synergy between shallow and deep learning accelerates the materials discovery pipeline by optimizing data utilization, guiding experiments, and reducing research costs. Together, these AI-driven approaches are transforming renewable energy research, enabling faster, more efficient identification of sustainable materials for the global energy transition.