The urgent demand for clean energy and environmental sustainability calls for the rapid development of advanced materials with tailored properties. Traditional trial-and-error approaches are often time-consuming and resource-intensive. In recent years, artificial intelligence (AI) has emerged as a transformative tool in accelerating the discovery, design, and optimization of sustainable materials for applications such as catalysis, CO₂ capture, energy storage, water purification, and beyond. This Research Topic aims to showcase cutting-edge advances at the intersection of AI and materials science, emphasizing data-driven methods, inverse design, and closed-loop experimentation. We welcome original research, reviews, and perspective articles that demonstrate novel AI techniques, databases, or frameworks accelerating the development of materials for energy and environmental applications.

The discovery and development of new sustainable materials are pivotal for transitioning toward renewable energy and mitigating environmental impacts. However, conventional experimental approaches are typically resource-intensive and slow, limiting the rapid deployment of advanced materials needed to address urgent environmental challenges. Artificial Intelligence (AI) techniques, particularly machine learning, offer transformative potential to accelerate the design and optimization of sustainable materials by efficiently navigating vast chemical spaces, predicting material properties, and guiding experimentation.

This Research Topic aims to address critical bottlenecks in sustainable materials discovery by showcasing cutting-edge AI-driven methodologies. We seek to illustrate how data-driven modeling, inverse design, generative AI, and automated experimentation can expedite the identification of materials with superior performance, lower environmental footprint, and greater economic viability. Through collecting and disseminating high-quality research, we strive to stimulate interdisciplinary collaboration, encourage the integration of computational and experimental approaches, and ultimately accelerate progress toward sustainable energy and environmental solutions.

This Research Topic invites contributions that leverage AI and ML to accelerate the discovery, design, and deployment of sustainable materials for energy and environmental applications. We welcome submissions focused on AI-guided approaches in areas such as catalysis, CO₂ capture, fuel cells, energy storage (e.g., batteries, supercapacitors), water treatment, membranes, photovoltaics, and green chemistry.

Key topics of interest include, but are not limited to:
1. AI/ML models for predicting physical, chemical, or functional properties of materials
2. Generative models and inverse design for materials with target characteristics
3. Autonomous laboratories and closed-loop optimization Integration of AI with high-throughput experiments or simulations
4. AI-assisted screening and selection of low-impact, renewable, or recyclable materials
5. Multi-objective optimization including life cycle and techno-economic metrics
6. Data infrastructure, materials databases, and open-source tools supporting sustainable development
7. Interpretable and trustworthy AI models for physical science applications

We encourage original research articles, reviews, perspectives, and methodological papers. Submissions should emphasize scientific rigor, reproducibility, and potential for real-world impact. Interdisciplinary work that bridges AI, materials science, and sustainability is particularly welcome.

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AI-Guided Discovery of Sustainable Materials for Energy and Environment