Artificial Intelligence for Advanced Materials in Food, Agriculture and Environmental Sustainability

Dear Colleagues,

The convergence of artificial intelligence (AI) and advanced materials science is transforming how researchers design, predict, and deploy functional materials to address some of the most pressing global challenges in food security, sustainable agriculture, and environmental protection. From AI-guided discovery of biodegradable packaging and smart sensors for food safety to machine learning-driven designs of nanomaterials for precision agriculture and pollutant remediation, this Topic aims to capture the rapidly expanding body of research at this multidisciplinary interface.

This Topic invites original research articles, reviews, and communications that explore how AI, machine learning, and computational modeling are accelerating the discovery, characterization, and application of advanced materials across the food–agriculture–environment continuum. Contributions bridging materials science, data science, food technology, agricultural engineering, environmental science, and regulatory/safety assessment are particularly welcome, as are studies addressing the safe and sustainable design (SSbD) of novel materials intended for these sectors.

This Topic welcomes contributions on (but not limited to) the following:

• AI/machine learning for the discovery and design of novel or functional materials (nanomaterials, biopolymers, composites, and smart/responsive materials);
• Predictive modeling of material performance, degradation, and environmental fate;
• AI-enabled sensors and biosensors for food quality, safety, and contaminant detection;
• Smart packaging, edible coatings, and controlled-release materials for food preservation;
• Materials informatics and high-throughput screening for agricultural applications (soil health, controlled-release fertilizers, and pesticide delivery);
• AI-assisted design of materials for water and soil remediation, pollutant capture, and environmental monitoring;
• Machine learning approaches to risk assessment, toxicology, and safety evaluation of advanced/nanomaterials (in silico methods, in vitro–in silico integration, and New Approach Methodologies);
• Safe and Sustainable by Design (SSbD) frameworks supported by AI/computational tools;
• Multi-omics and AI integration for understanding material–biological system interactions;
• Digital twins, process optimization, and AI-driven manufacturing of sustainable materials;
• Regulatory and policy perspectives on AI-supported material safety assessment (OECD, EFSA, ECHA, and FDA).

Dr. Ajay Vikram Singh
Dr. Alessandro Bertagnon
Topic Editors