Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2607.10388

The evolution of AI from image interpretation toward scientific inference in nanoparticle electron microscopy

Evropi Toulkeridou·Jiafei Li·Leonardo Lari·Panagiotis Grammatikopoulos
View original source ↗Read source document ↗Export metadata ↓

Abstract

Artificial intelligence (AI) is transforming electron microscopy by enabling quantitative analysis of increasingly large and complex datasets for nanoparticle characterization. Recent advances in machine learning (ML) and deep learning (DL) have expanded microscopy from a descriptive imaging technique into a data-driven platform for structural interpretation, dynamic analysis, and scientific inference. This review examines AI methodologies for nanoparticle electron microscopy, focusing on transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HRTEM), scanning transmission electron microscopy (STEM), and in situ TEM. The discussion is organized around the principal challenges in nanoparticle characterization, including particle detection, segmentation, morphology quantification, atomic-resolution restoration, defect identification, two-dimensional-to-three-dimensional structural inference, and analysis of dynamic processes in situ. We review computational approaches from conventional ML and convolutional neural networks to transformer architectures, self-supervised learning, foundation models, multimodal AI, and physics-informed learning. We further discuss integrating microscopy data with simulations, metadata, and autonomous experimentation to relate nanoparticle structure, dynamics, synthesis conditions, and functional properties. The advantages, limitations, benchmarking, and data requirements of current methodologies are critically assessed. Finally, emerging opportunities for foundation models, AI-guided microscopy, closed-loop experimentation, and autonomous materials discovery are discussed. By integrating advances across computer vision, materials informatics, and electron microscopy, this review highlights the role of AI in next-generation nanoparticle characterization and accelerated materials discovery.

Explore related subjects

cond-mat.mtrl-scics.AI

Keep this discovery

BibTeXRIS

Evropi Toulkeridou, Jiafei Li, Leonardo Lari, Panagiotis Grammatikopoulos. 2026-07-11. The evolution of AI from image interpretation toward scientific inference in nanoparticle electron microscopy. https://arxiv.org/abs/2607.10388

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Measuring chiral phonons

Chiral phonons are quantized vibrations where the atomic motion in a solid breaks improper rotation symmetries. In many cases, chiral phonons possess angular momenta and are therefore selective to circularly polarized light. Both fundamental and applied research efforts on chiral phonons have been gaining increasing attention owing to their importance in a variety of fields including spintronics, spin-selective chemical reactions, thermal transport, quantum information processing and biosensing, where the bi-directional spin-lattice coupling enabled by chiral phonons can be harnessed in new ways, and potentially lead to new functionalities. Thus far, the studies of chiral phonons across diverse materials platforms have evolved largely independently within these fields, but the experimental techniques are often interrelated. In this perspective, we present a detailed description, as well as advantages and disadvantages of the current approaches for experimentally measuring chiral phonons in chiral and achiral materials. We conclude with a discussion of new methods for measuring chiral phonons. Ultimately, this work seeks to offer an experimental guide for systematically investigating the properties of chiral phonons in various materials systems and applications.

cond-mat.mtrl-sci↗
arXiv2026

A model of grain growth in UN integrating molecular dynamics, phase-field modeling, and uncertainty quantification

Grain growth kinetics and grain-boundary (GB) properties in uranium mononitride (UN) are investigated through an integrated multiscale framework combining molecular dynamics (MD), phase-field modeling, and surrogate-assisted uncertainty quantification. MD simulations yield GB energies for 27 symmetric tilt boundaries from 0--2000~K, which are consistent with available DFT values. The average GB energy is nearly temperature-independent below 1000~K and increases at higher temperatures. A mechanistic pore-drag model applied to the only available grain growth dataset for actinide nitrides yields a mobility reduction factor of $s \approx 0.93$--$0.99$, statistically indistinguishable from unity, confirming that pore drag is negligible under the experimental conditions. The intrinsic GB mobility is therefore extracted directly from the effective mobility, yielding $M_0 = 2.05\times10^{-15}$~m$^4$/(J$\cdot$s) and $Q_M = 0.89$~eV. Phase-field simulations conducted from 1500--2000~K confirm normal curvature-driven grain growth, with grain size distributions converging to the Hillert-like form. A surrogate-assisted global sensitivity analysis---combining principal component analysis, Gaussian process regression, and Sobol decomposition---reveals that the mobility prefactor $M_0$ dominates output variance at all times, followed by the activation energy $Q_M$, while the GB energy $\gamma$ contributes minimally. These results establish the first quantitative grain growth framework for UN and identify the reduction of uncertainty in $M_0$ and $Q_M$ as the highest-priority target for future experimental efforts.

cond-mat.mtrl-sci↗
arXiv2026

Silicon Solar Cell Design for >30% Efficiency via Singlet Fission

Singlet fission (SF) materials convert high-energy photons into multiple charge carriers, providing a route to exceed the efficiency limits of single-junction silicon solar cells without many of the complexities of multi-junction tandem designs. Following the first demonstration of an SF-enhanced silicon solar cell in 2025, there is a need to understand how SF materials can be effectively integrated into high-efficiency industrial silicon devices and translated from proof of concept to a manufacturable technology. Using coupled optical and electrical simulations, we assess the efficiency potential of several industrially relevant silicon cell architectures combined with SF materials. Interdigitated back-contact (IBC) cells offer the greatest potential for improvement due to unrestricted front-surface access and can achieve efficiencies exceeding 33%. However, performance is highly sensitive to front-surface passivation quality. Appropriate silicon design, particularly controlled surface doping and fixed interfacial charge, can mitigate recombination losses and relax passivation requirements for ultra-thin exciton-transfer layers.

cond-mat.mtrl-sci↗