Aug 2026· Advanced Intelligent Systems· 0 citations· 75 references
TL;DR
This Perspective examines how recent advances in data‐driven modeling, high‐performance simulation, and autonomous experimentation are converging to accelerate the discovery of functional materials for next‐generation technologies—from energy storage and biomedicine to nanoelectronics and quantum devices.
Abstract
Artificial intelligence (AI) is transforming the way materials are designed, understood, and manufactured. This Perspective examines how recent advances in data‐driven modeling, high‐performance simulation, and autonomous experimentation are converging to accelerate the discovery of functional materials for next‐generation technologies—from energy storage and biomedicine to nanoelectronics and quantum devices. We outline ongoing strategies to embed AI across the materials design workflow—from synthesis and characterization to large‐scale simulations enabled by machine learning techniques and approaching ab initio accuracy—and discuss key challenges that remain on the path toward intelligent (bio)materials discovery.
This work will present the current work on inverse material design, where AI methods—particularly generative pretrained transformers—are used to predict new material candidates based on desired properties, pushing the boundaries of materials innovation.
I. Gonzales, R. Ullberg, Andrew H Salij et al.· ECS Meeting Abstracts· 0 citations
This review examines how AI methodologies, ranging from machine learning‐assisted first‐principles simulations to deep‐learning analysis of experimental data, are reshaping the study of HfO‐based ferroelectrics to enable predictive design and autonomous optimization of next‐generation hafnia‐based ferroelectrics.
Faizan Ali, D. Lehninger, F. Sánchez et al.· Advanced Electronic Material...· 0 citations
Nanotechnology and Artificial Intelligence (AI) have separately been
revolutionizing the world of scientific and industrial innovation. The combination of both has
resulted in fast material discovery, improved diagnostic abilities, intelligent therapeutic systems, and advanced computational systems. However, the current literature has presented a gap
in structured evaluations of the impact of AI methodologies on nanoscale research and the role
of nanoscale materials in the development of next-generation AI platforms
The structured review approach was adopted in this study to identify peer-reviewed
articles from prominent scientific databases (Scopus, Web of Science, IEEE Xplore, PubMed)
between 2015 and 2025. A pre-defined search strategy and inclusion-exclusion criteria were
used. The articles were screened to evaluate the comparison of AI models, nanomaterial applications, and performance trends.
The review shows that AI methods, such as Convolutional Neural Networks, Graph
Neural Networks, and Transformers, greatly improve nanoscale design, prediction, and characterization. AI-assisted nanotechnology advances the accuracy of simulations, minimizes the
number of experiments, and facilitates optimal drug delivery routes, biosensing, and material
properties prediction. On the other hand, nanoscale materials advance energy-efficient AI
hardware, neuromorphic computing, and high-performance computing infrastructure.
The results clearly show that there is a mutual technological front where AI advances nanotechnology and nanoscale materials advance energy-efficient AI hardware. The
current limitations include a lack of data, a lack of interpretability of AI models, a lack of
benchmarking standards, and the need for ethical and regulatory frameworks.
The field of AI and nanotechnology is a rapidly growing interdisciplinary area
with great potential for scientific and societal impact.
Arsheen Kaur, Malti Rani, Himali Sarangal et al.· Recent Advances in Computer...· 0 citations
The convergence of artificial intelligence (AI) and nanotechnology has substantially transformed the discovery, design, synthesis, characterization and biomedical application of nanomaterials. This review provides a structured overview of AI applications in nanotechnology, including data-driven nanomaterial discovery and inverse design using machine learning (ML) and generative models, optimization of nanoparticle synthesis through Bayesian optimization and self-driving laboratories, targeted drug delivery and personalized nanomedicine enabled by predictive ML models, deep learning for nanoscale imaging, spectroscopy and real-time particle tracking, AI-accelerated simulation of nanofluids and complex nanosystems, together with the emerging challenges, opportunities and future directions of AI-driven nanotechnology. We discuss the capabilities and limitations of widely used AI methods, including artificial neural networks, random forests, support vector machines, reinforcement learning, generative adversarial networks, variational autoencoders, graph neural networks and large language models, highlighting their suitability for different nanotechnology applications. In addition, the review examines key challenges that currently limit broader translation of AI-enabled nanotechnologies, including limited availability of standardized high-quality datasets, model interpretability, reproducibility, validation across independent datasets and regulatory considerations. Finally, we discuss emerging research directions, including autonomous experimentation, multiscale AI frameworks, AI-assisted nanorobotic systems and closed-loop therapeutic platforms, emphasizing that these represent promising future opportunities requiring further technological development and rigorous experimental and clinical validation.
Chemical research is no longer confined strictly to the lab bench or trial and error. Artificial
intelligence is transforming the field, helping to predict molecular behavior, find potential designs,
and automate certain aspects of the discovery process, introducing a new kind of intuition. Over
the past decade, advances in machine learning, natural language processing, robotics, and automation
have enabled new areas of research. These are broadening the applications for retrosynthetic analysis,
reaction optimization, and computer-aided synthetic planning. This study examines the evolution of
computer-aided synthesis, describing its development from rule-based approaches to advanced deep
learning and hybrid systems that leverage large datasets. Thus, it focuses on AI platforms that integrate
predictive algorithms with rapidly evolving robotic systems. Such technologies enable rapid
hypothesis generation, reaction screening, and the improvement of synthetic methods. The review
encompasses synthesis analysis tools, recommendation algorithms, and autonomous labs that deliver
discoveries more quickly and minimize waste and environmental impact. It examines current challenges,
such as data scarcity, sporadic reporting, model interpretability, and practical applications.
More broadly, the need for sustainable, collaborative research has increased, and cross-border work
through cloud-based laboratories and shared databases enables chemists worldwide to share resources.
The review identifies beneficial trends and ongoing challenges, with a view to providing opportunities
for AI to make chemistry greener, accelerate discovery, and improve decision-making across academic
and industrial settings. AI is not replacing chemists but rather enhancing creativity and intuition,
bringing together research that traditional methods would never have allowed, on a scale never before
possible without AI.
Rizvee Ahmad Samir, Yu-Meng Zhang, Zi-Shan Xu et al.· Letters in Organic Chemistry· 0 citations
This review examines emerging AI methodologies for accelerated materials discovery, with particular emphasis on how computational design, data infrastructure, synthesis planning, and autonomous experimentation can be connected into experimentally grounded workflows.
Jaehwan Choi, Seongmin Kim, Junkil Park et al.· Chemical Reviews· 1 citation