This review traces the development of the field from classical machine learning and deep learning to generative models, transfer learning, transformers, and emerging foundation models, and introduces major nanophotonic platforms.
Abstract
Artificial intelligence (AI) is increasingly used to model, design, and study nanophotonic systems. This review traces the development of the field from classical machine learning and deep learning to generative models, transfer learning, transformers, and emerging foundation models. It first introduces major nanophotonic platforms, including nanoparticles, nanoholes, metasurfaces, photonic crystals, multilayer thin films, and integrated photonic devices, together with their main forward and inverse problems. It then reviews data-driven methods for predicting optical spectra and fields, generating structures from target responses, improving designs through optimization, and accounting for fabrication constraints. Generative models are discussed as a way to produce multiple valid solutions to nonunique inverse problems, while transfer learning, few-shot learning, and physics-aware training help reduce data requirements and improve generalization. Recent domain-specific foundation models show that different optical structures and responses can be handled within shared representations, but current systems remain limited in scope and physical grounding. Future progress will depend on multimodal models that connect geometry, materials, spectra, electromagnetic (EM) fields, fabrication data, experiments, and scientific literature with reliable simulation and validation tools. Current foundation models remain domain-specific, and their extension to broader nanophotonic tasks will require stronger physical grounding and validation.
This review surveys how Large Language Models are adding semantic interfaces, code generation, and tool orchestration to established numerical nanophotonic workflows, and looks ahead to the next generation of multimodal foundation models with physical perception capabilities.
Huanshu Zhang, Kegeng Tang, Lei Kang et al.· 1 citation
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
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.
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
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
In this lecture, I review our recent progress within our ARTISTIC research initiative regarding the development of Physics-Informed Deep Learning frameworks to unravel the complex links between manufacturing parameters and battery electrode properties. While physics-based simulations (such as Coarse-Grained Molecular Dynamics or Discrete Element Method) offer deep insights into the formation of electrode microstructures, their high computational cost often hinders their direct use in real-time optimization loops.
Here, I will demonstrate how we bridge this gap by merging AI with physics-based simulations. We utilize high-fidelity 3D physical models to generate synthetic datasets describing electrode formation steps, such as slurry drying and calendering. These datasets are then used to train Deep Learning networks that capture the underlying process-structure correlations. The resulting Deep Learning models act as ultra-fast surrogates capable of predicting the dynamics leading to the 3D microstructures of Lithium-ion battery electrodes, including the spatial location of active material, carbon-binder and pores. These predictions are experimentally validated against key metrics, such as porosity, tortuosity factor, and effective conductivities.
I will illustrate how these fast surrogate models are coupled with macroscale models in order to perform multiscale modeling of manufacturing processes, and to numerical optimizers to solve inverse design problems, predicting the optimal manufacturing process parameters required to achieve specific electrode architectures. In addition to microstructural formation, I will also discuss our work on simulating electrolyte wetting phenomena by combining Lattice Boltzmann Method with AI, a critical step linking the manufactured electrodes to their electrochemical activity and aging processes.
Furthermore, I will discuss the integration of experimental data from our pilot line into this workflow to ensure physical relevance, paving the way towards high-throughput screening and digital twins for battery manufacturing.
Finally, I will also illustrate how we are extending these computational modeling concepts to the structural design of Redox Flow Battery electrodes.
References
[1]
Prof. Alejandro A. Franco's group publications: https://www.modeling-electrochemistry.com/publications
[2]
Galvez-Aranda, D. E., Fernandez, F., & Franco, A. A. (2025). Physics-Assisted Machine Learning for the Simulation of the Slurry Drying in the Manufacturing Process of Battery Electrodes: A Hybrid Time-Dependent VGG16-DEM Model.
ACS Applied Materials & Interfaces
,
17
(22), 32150-32162.
[3]
Galvez‐Aranda, D. E., Dinh, T. L., Vijay, U., Zanotto, F. M., & Franco, A. A. (2024). Time‐Dependent Deep Learning Manufacturing Process Model for Battery Electrode Microstructure Prediction.
Advanced Energy Materials
,
14
(15), 2400376.
[4]
Vijay, U., Fernandez, F., Ben Hadj Ali, S., Asch, M., & Franco, A. A. (2025). Surrogate Modeling of Lithium‐Ion Battery Electrode Manufacturing by Combining Physics‐Based Simulation and Deep Learning.
Batteries & Supercaps
, e202500433.
[5]
Vijay, U., Galvez-Aranda, D. E., Zanotto, F. M., Le-Dinh, T., Alabdali, M., Asch, M., & Franco, A. A. (2025). A hybrid modelling approach coupling physics-based simulation and deep learning for battery electrode manufacturing simulations.
Energy Storage Materials
,
75
, 103883.
[6]
Troncoso, J. F., Zanotto, F. M., Galvez‐Aranda, D. E., Zapata Dominguez, D., Denisart, L., & Franco, A. A. (2025). The ARTISTIC battery manufacturing digitalization initiative: from fundamental research to industrialization.
Batteries & Supercaps
,
8
(1), e202400385.
Figure 1
Alejandro A. Franco· ECS Meeting Abstracts· 0 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.