Hugging Face Trending Papers

Enhancing EBSD throughput of battery electrode materials using super-resolution generative adversarial networks

The paper presents a super‑resolution generative adversarial network (SRGAN) that boosts the throughput of electron backscatter diffraction (EBSD) for Li‑ion battery electrode materials. By training on LiNixMnyCozO2 cathode data, the SRGAN outperforms classical interpolation across upscaling factors of 2× to 12×, especially preserving small grains and realistic boundaries. A 5× upscaling yields a 25× speed‑up or larger field of view with acceptable accuracy, reducing grain‑size errors to within ±15%.

arXiv Machine Learning

Aug 20

Enhancing EBSD throughput of battery electrode materials using super-resolution generative adversarial networks

The paper presents a super‑resolution generative adversarial network (SRGAN) that boosts electron backscatter diffraction (EBSD) throughput for lithium‑ion battery electrode materials. Trained on LiNixMnyCozO2 cathode data, the SRGAN outperforms classical interpolation across 2×–12× upscaling, especially preserving small grains and realistic boundaries. A 5× upscaling yields a 25× speed‑up or larger field of view with acceptable errors in grain size and shape metrics.

By John Mangum, Andrew Glaws, Francois Usseglio-Viretta, Steven Spurgeon, Donal Finegan

efficiency safety

arXiv Computer Vision

Sep 15

Multi4D: an end-to-end neural network for structural determination at complex material interfaces

arXiv:2609.14348v1 Announce Type: cross Abstract: Heterogeneous interfaces dictate the performance and degradation of functional materials, making it essential to link local structural variations wit...

By Haoran Zhang, Zian Mao, Shufen Chu, Xiaoya He, Yuyan Guan, Antong Yang, Mingze Li, Xiaoqin Zeng, Yujun Xie

llms diffusion

arXiv AI

Jul 14

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

arXiv:2607. 10388v1 Announce Type: cross Abstract: Artificial intelligence (AI) is transforming electron microscopy by enabling quantitative analysis of increasingly large and complex datasets for nanoparticle characterization.

By Evropi Toulkeridou, Jiafei Li, Leonardo Lari, Panagiotis Grammatikopoulos

llms agents computer-vision multimodal benchmarks

arXiv Machine Learning

2d ago

A Digital Simulation Toolkit for Physics-Based Generation of Realistic Experimental Scanning Tunneling Microscopy Images

arXiv:2609.36639v1 Announce Type: new Abstract: Scanning Tunneling Microscopy (STM) is a widely used tool for characterizing surfaces of materials at the atomic scale, playing a crucial role in disco...

By Huanhuan Zhao, Laxmi Bhurtel, Connor Vernachio, Fahmy Paiziah, Wonhee Ko, Arpan Biswas

arXiv Machine Learning

Aug 28

Data-efficient crack quantification in lithium-ion cathodes using foundation model transfer

The paper presents a data‑efficient method for quantifying cracks in lithium‑ion cathodes using a frozen self‑supervised vision‑transformer encoder, a lightweight decoder, and iterative model‑assisted annotation. Applied to three 120‑megapixel NMC cathode cross‑sections, the framework distinguishes intragranular from early and late intergranular cracks, providing per‑particle distributions of crack width, tortuosity, and area fraction. Late intergranular crack coverage reaches 4.6% in cycled samples versus 0.5% in initial and calendar‑aged samples, indicating degradation primarily from electrochemical cycling.

By Thorsten Tegetmeyer-Kleine, Thomas Schmitt, Phillip Aquino, Christiane Rahe, Dirk Uwe Sauer, Weihan Li

llms

arXiv Machine Learning

Jun 29

Physics-Informed Neural Network with Transfer Learning for State Estimation in Lithium-Ion Batteries using the Single Particle Model with Electrolyte

arXiv:2606. 28220v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving nonlinear partial differential equations (PDEs), including battery electrochemical models.

By Gift Modekwe, Qiugang Lu

diffusion fine-tuning

arXiv Machine Learning

Jun 30

PCP-GAN: Property-Constrained Pore-scale image reconstruction via conditional Generative Adversarial Networks

arXiv:2510. 19465v2 Announce Type: replace-cross Abstract: Obtaining truly representative pore-scale images that match bulk formation properties remains a fundamental challenge in subsurface characterization, as natural spatial heterogeneity causes extracted sub-images to deviate significantly from core-measured values.

By Ali Sadeghkhani, Brandon Bennett, Masoud Babaei, Arash Rabbani

diffusion safety

arXiv AI

Jun 2

Bridging the Sim-to-Real Gap in Semiconductor Visual Program Synthesis via Input Binarization

arXiv:2606. 02434v1 Announce Type: new Abstract: Precise parametric control over circuit geometry is essential for semiconductor inspection, yet obtaining sufficient real training data remains costly.

By Yusuke Ohtsubo, Kota Dohi, Koichiro Yawata, Koki Takeshita, Tatsuya Sasaki

llms diffusion robotics multimodal safety

arXiv Machine Learning

Jun 10

Unsupervised Deep Learning for Limited-Angle STEM-EDX Tomography -- Application to 3D Chemical Analysis of Phase-Change Memory Devices

arXiv:2606. 10547v1 Announce Type: cross Abstract: Energy Dispersive X-ray (EDX) tomography in Scanning Transmission Electron Microscopy (STEM) enables 3D compositional and elemental mapping at the nanoscale, but its use is limited by restricted tilt ranges and low-dose conditions required to avoid beam damage.

By Daniel del Pozo Bueno, Serge Brosset, Theo Monniez, Gabriele Navarro, Philippe Ciuciu, Zineb Saghi

arXiv Machine Learning

Jun 2

Machine Learning Surrogate Modeling for Homogenization of Hyperelastic Materials with Boolean Microstructures

arXiv:2606. 00938v1 Announce Type: cross Abstract: Data-driven surrogate models are an alternative to numerical homogenization of heterogeneous materials.

By Matthias Br\"andel, Oliver Rheinbach

arXiv Machine Learning

Sep 25

Leakage-Safe Machine Learning for Hydrogen Embrittlement Detection in 316L Stainless Steel: A Region-Held-Out Evaluation of Texture and Deep Features in SEM Micrographs

The study introduces a leakage‑safe evaluation protocol for detecting hydrogen embrittlement in 316L stainless steel using SEM micrographs. By employing a Leave‑One‑Region‑Out cross‑validation over 14 spatial regions, the authors compare six feature‑classifier combinations, finding that a simple local binary pattern (LBP) with a support vector machine (SVM) achieves the best performance (balanced accuracy 0.79, H2 recall 0.69, H2 precision 0.82). The results are statistically significant (p = 0.008) and suggest that texture descriptors can reliably recover hydrogen‑charging signatures even with limited data.

By Muhammad Awais, Muhammad Yaseen, Abdul Shakoor, Niaz Ahmed Niaz, Huria Zia, Muhammad Zain Shakoor

rag safety

arXiv Machine Learning

Jul 24

Machine Learning for Charge State Characterization of Isolated Double Quantum Dots

arXiv:2607. 20871v1 Announce Type: cross Abstract: Scaling semiconductor quantum dot arrays toward fault-tolerant quantum computing requires efficient tuneup of spin qubits, a process that depends on the analysis of charge stability maps (CSMs) and remains largely manual.

By Hyma Vallabhapurapu, Marco Candido, Krishna Choudhary, Paul Steinacker, Ensar Vahapoglu, Chris Escott, Wee Han Lim, Andre Saraiva, Nard Dumoulin Stuyck, MengKe Feng

fine-tuning

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