Safe and sustainable by design with ML/AI: A transformative approach to advancing nanotechnology

• Georgia Melagraki

Beilstein J. Nanotechnol. 2026, 17, 176–185, doi:10.3762/bjnano.17.11

• materials before producing them. This perspective article highlights how ML and AI are driving the evolution of SSbD in nanotechnology, focussing on predictive toxicology, materials informatics, lifecycle analysis, and the pivotal role of digital twins. It also explores current challenges, emerging

• opportunities, and the path forward for integrating ML/AI-driven SSbD frameworks into regulatory and industrial practices. Keywords: digital twins; machine learning; materials informatics; safe and sustainable by design; Introduction Nanotechnology has fundamentally changed the landscape of materials science

• . By illuminating the hidden risks that can arise across the lifespan of a material, they help ensure that nanotechnological innovations do not inadvertently compromise human health or ecological balance. Digital twins in safe by design Digital twins represent a significant leap in SSbD methodologies

Missing links in nanomaterials research impacting productivity and perceptions

• Santosh K. Tiwari and
• Nannan Wang

Beilstein J. Nanotechnol. 2025, 16, 2168–2176, doi:10.3762/bjnano.16.149

• capital by training engineers specialized in nanotechnology, while nurturing interdisciplinary research to drive fundamental discoveries. (7) Integration of artificial intelligence, machine learning, and digital twins can accelerate materials discovery, predict safety and performance, and guide scale-up

Integrating high-performance computing, machine learning, data management workflows, and infrastructures for multiscale simulations and nanomaterials technologies

• Fabio Le Piane,
• Mario Vozza,
• Matteo Baldoni and
• Francesco Mercuri

Beilstein J. Nanotechnol. 2024, 15, 1498–1521, doi:10.3762/bjnano.15.119

• across a diverse range of tools. The paper explores the distinctive features of digital and data-centric approaches and technologies for materials development. It highlights the role of digital twins in research, particularly in the realm of nanomaterials development and examines the impact of knowledge

• integration technologies, outlining their role as key enablers for the realization of digital twins (DTs) in the context of materials and nanomaterials development. We will also showcase some of the work carried out towards these goals, illustrating the main principles behind the development of tools and

• approaches. The paper is structured as follows: The first section revolves around data-centric approaches for materials development, emphasizing the pivotal role of data; the second section is about the realization of digital twins of nanomaterials, elucidating conceptualization and implementation; the third