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Safe and sustainable by design with ML/AI: A transformative approach to advancing nanotechnology
1Hellenic Military Academy, Vari, Greece.
Beilstein Journal of Nanotechnology
|January 22, 2026
Summary
Machine learning and artificial intelligence are advancing the Safe and Sustainable by Design (SSbD) concept for nanotechnology. These technologies enable predictive modeling and risk assessment, ensuring safer and more sustainable nanomaterial development.
Area of Science:
- Nanotechnology
- Materials Science
- Environmental Science
Background:
- Nanotechnology offers transformative applications across medicine, energy, and environmental sectors.
- Innovations in nanomaterials present complex safety and sustainability challenges.
- The Safe and Sustainable by Design (SSbD) concept integrates safety and sustainability early in development.
Purpose of the Study:
- To highlight the role of machine learning (ML) and artificial intelligence (AI) in advancing SSbD for nanotechnology.
- To explore how ML/AI enhance predictive toxicology, materials informatics, and lifecycle analysis.
- To discuss challenges and opportunities for integrating ML/AI-driven SSbD frameworks.
Main Methods:
- Review of current literature and advancements in ML/AI applications for SSbD.
- Focus on predictive modeling, risk assessment, and computational screening of nanomaterials.
- Exploration of digital twins and lifecycle analysis within SSbD frameworks.
Main Results:
- ML and AI significantly enhance SSbD by providing predictive capabilities for safety and sustainability.
- These technologies enable proactive risk mitigation and efficient material screening.
- Digital twins and advanced analytics are crucial for comprehensive lifecycle assessment.
Conclusions:
- ML/AI are pivotal in evolving SSbD for safer and sustainable nanotechnology.
- Integration of ML/AI-driven SSbD frameworks is essential for regulatory and industrial adoption.
- Continued research is needed to address challenges and seize opportunities in this domain.
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