Artificial Intelligence–Driven Frameworks for Accelerated Materials Discovery in Energy and Sustainability Applications

Abstract

The discovery and optimization of advanced materials for energy and sustainability applications have traditionally relied on time-intensive experimental and computational workflows. Recent advances in artificial intelligence (AI), particularly machine learning and data-driven modeling, have enabled a paradigm shift toward accelerated materials discovery. This paper presents a comprehensive AI-driven framework designed to streamline the identification, prediction, and optimization of functional materials for energy storage, conversion, and sustainable infrastructure applications. The proposed framework integrates materials databases, feature engineering, supervised and unsupervised learning algorithms, and iterative validation loops to reduce experimental cost and discovery time. Case-oriented analyses illustrate how AI models can predict key material properties such as bandgap, thermal stability, catalytic activity, and mechanical performance with high accuracy. Furthermore, the study highlights the role of explainable AI in enhancing model transparency and guiding experimental decision-making. Challenges related to data scarcity, model generalization, and integration with laboratory workflows are critically discussed. The findings demonstrate that AI-driven materials discovery not only accelerates innovation cycles but also supports sustainable development goals by enabling the rapid deployment of eco-friendly and high- performance materials. This research contributes a scalable and adaptable framework suitable for next-generation energy and sustainability-oriented materials research.