Green and Sustainable Chemistry Meets AI: Accelerating Innovation for a Greener Future

Green and Sustainable Chemistry aims to design chemical products and processes that reduce or eliminate the use and generation of hazardous substances, contributing to a healthier environment and more resource-efficient society. Traditionally, the development of sustainable chemical processes has been a complex and iterative endeavor, often requiring extensive experimentation. Recently, artificial intelligence (AI) has emerged as a transformative tool in green chemistry, enabling data-driven insights into reaction design, material selection, and life cycle optimization. By applying machine learning algorithms and advanced modeling techniques, researchers can predict environmental impact, optimize synthetic routes, and discover eco-friendly alternatives with greater speed and accuracy. This convergence of AI and sustainable chemistry opens new avenues for achieving environmental targets while maintaining economic viability.

This Research Topic aims to explore the synergies between artificial intelligence and Green and Sustainable Chemistry, highlighting recent innovations and interdisciplinary approaches. The objective is to provide a dynamic platform for researchers to showcase how AI is revolutionizing the development of sustainable chemical processes—by optimizing synthetic strategies, selecting environmentally benign materials, and evaluating full life cycle impacts. Leveraging AI not only accelerates discovery but also enhances the sustainability of chemical systems by enabling data-informed decisions that align with the 12 principles of green chemistry. This collection seeks to promote a more circular and resource-efficient chemical industry, encouraging contributions that balance scientific innovation with ecological responsibility.

We welcome Original Research, Review, Mini Review, and Perspective articles on topics including, but not limited to:

• AI-guided design of green synthetic methodologies
• Machine learning for solvent and reagent selection with minimal environmental impact
• Predictive tools for assessing process sustainability and life cycle metrics
• Data-driven identification of alternative, renewable feedstocks
• Applications of AI in waste reduction, energy efficiency, and carbon footprint minimization
• Challenges and future directions in integrating AI with green chemistry principles

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• Mini Review
• Original Research
• Perspective
• Review

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• Mini Review
• Original Research
• Perspective
• Review

Keywords: Green Chemistry; Artificial Intelligence; Sustainable Processes; Life Cycle Analysis; Eco-friendly Design

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.