Tootfinder

Quantum Metrology of Spin Sensing with Free Space Electrons
Santiago Beltr\'an-Romero, Michael Gaida, Philipp Haslinger, Dennis R\"atzel, Stefan Nimmrichter
https://arxiv.org/abs/2509.14982

Quantum Metrology of Spin Sensing with Free Space Electrons
Recent advances in transmission electron microscopy (TEM) have opened the path toward spin resonance spectroscopy with single-spin sensitivity. To assess this potential, we investigate the quantum precision limits for sensing magnetic moments with free-electron probes. Using a scattering model where an electron wavepacket interacts with a localized spin, we study two metrological tasks: estimating the magnitude of the magnetic moment and discriminating the presence of a spin. The sensitivity fo…

S1-MatAgent: A planner driven multi-agent system for material discovery
Xinrui Wang, Chengbo Li, Boxuan Zhang, Jiahui Shi, Nian Ran, Linjing Li, Jianjun Liu, Dajun Zeng
https://arxiv.org/abs/2509.14542

S1-MatAgent: A planner driven multi-agent system for material discovery
The discovery of high-performance materials is crucial for technological advancement. Inverse design using multi-agent systems (MAS) shows great potential for new material discovery. However, current MAS for materials research rely on predefined configurations and tools, limiting their adaptability and scalability. To address these limitations, we developed a planner driven multi-agent system (S1-MatAgent) which adopts a Planner-Executor architecture. Planner automatically decomposes complex ma…

LamiGauss: Pitching Radiative Gaussian for Sparse-View X-ray Laminography Reconstruction
Chu Chen, Ander Biguri, Jean-Michel Morel, Raymond H. Chan, Carola-Bibiane Sch\"onlieb, Jizhou Li
https://arxiv.org/abs/2509.13863

LamiGauss: Pitching Radiative Gaussian for Sparse-View X-ray Laminography Reconstruction
X-ray Computed Laminography (CL) is essential for non-destructive inspection of plate-like structures in applications such as microchips and composite battery materials, where traditional computed tomography (CT) struggles due to geometric constraints. However, reconstructing high-quality volumes from laminographic projections remains challenging, particularly under highly sparse-view acquisition conditions. In this paper, we propose a reconstruction algorithm, namely LamiGauss, that combines G…

Data-driven discovery of governing equation for sheared granular materials in steady and transient states
Xu Han, Lu Jing, Chung-Yee Kwok, Gengchao Yang, Yuri Dumaresq Sobral
https://arxiv.org/abs/2509.14518

Data-driven discovery of governing equation for sheared granular materials in steady and transient states
Granular material has significant implications for industrial and geophysical processes. A long-lasting challenge, however, is seeking a unified rheology for its solid- and liquid-like behaviors under quasi-static, inertial, and even unsteady shear conditions. Here, we present a data-driven framework to discover the hidden governing equation of sheared granular materials. The framework, PINNSR-DA, addresses noisy discrete particle data via physics-informed neural networks with sparse regression…

Hybrid Deep Reconstruction for Vignetting-Free Upconversion Imaging through Scattering in ENZ Materials
Hao Zhang, Yang Xu, Wenwen Zhang, Saumya Choudhary, M. Zahirul Alam, Long D. Nguyen, Matthew Klein, Shivashankar Vangala, J. Keith Miller, Eric G. Johnson, Joshua R. Hendrickson, Robert W. Boyd, Sergio Carbajo
https://arxiv.org/abs/2508.…

Hybrid Deep Reconstruction for Vignetting-Free Upconversion Imaging through Scattering in ENZ Materials
Optical imaging through turbid or heterogeneous environments (collectively referred to as complex media) is fundamentally challenged by scattering, which scrambles structured spatial and phase information. To address this, we propose a hybrid-supervised deep learning framework to reconstruct high-fidelity images from nonlinear scattering measurements acquired with a time-gated epsilon-near-zero (ENZ) imaging system. The system leverages four-wave mixing (FWM) in subwavelength indium tin oxide (…

Mapping Microstructure: Manifold Construction for Accelerated Materials Exploration
Simon A. Mason, Megna N. Shah, Jeffrey P. Simmons, Dennis M. Dimiduk, Stephen R. Niezgoda
https://arxiv.org/abs/2509.15022

Mapping Microstructure: Manifold Construction for Accelerated Materials Exploration
Accelerating materials development requires quantitative linkages between processing, microstructure, and properties. In this work, we introduce a framework for mapping microstructure onto a low-dimensional material manifold that is parametrized by processing conditions. A key innovation is treating microstructure as a stochastic process, defined as a distribution of microstructural instances rather than a single image, enabling the extraction of material state descriptors that capture the esse…

Hybrid Fiber-Free-Space Entanglement Distribution Using Off-the-Shelf Quantum Devices
Gustavo C. Amaral, Nienke M. ten Haaf, Breno Perlingeiro, David L. Bakker, Mark G. M. Boekel, Tim E. van Duivenbode, Karthik Selvan, Nicolas Oidtmann, Rafael Ochsendorf, Rick N. M. Wasserman, Mael Flament, Felipe Giraldo, Shane Andrewski, Mehdi Namazi, Federica Facchin, Mario Casta\~neda, Fokko de Vries, Sayali Shevate, Shaurya Bhave, Marco Gorter, Nico Coesel, David Mytling, Mike Mabry, Carlo Page, A…

Hybrid Fiber-Free-Space Entanglement Distribution Using Off-the-Shelf Quantum Devices
Entanglement serves as a fundamental resource for quantum technologies, enabling communication and computation tasks that surpass classical limits. Its distribution across networks is essential for interconnecting quantum processors, enabling distributed quantum computing to address complex challenges in areas such as drug discovery, material science, and optimization. In this work, we report the successful distribution of polarization-entangled photon pairs across a campus-scale, three-node qu…

OpenConstruction: A Systematic Synthesis of Open Visual Datasets for Data-Centric Artificial Intelligence in Construction Monitoring
Ruoxin Xiong, Yanyu Wang, Jiannan Cai, Kaijian Liu, Yuansheng Zhu, Pingbo Tang, Nora El-Gohary
https://arxiv.org/abs/2508.11482

OpenConstruction: A Systematic Synthesis of Open Visual Datasets for Data-Centric Artificial Intelligence in Construction Monitoring
The construction industry increasingly relies on visual data to support Artificial Intelligence (AI) and Machine Learning (ML) applications for site monitoring. High-quality, domain-specific datasets, comprising images, videos, and point clouds, capture site geometry and spatiotemporal dynamics, including the location and interaction of objects, workers, and materials. However, despite growing interest in leveraging visual datasets, existing resources vary widely in sizes, data modalities, anno…

Generalized invariants meet constitutive neural networks: A novel framework for hyperelastic materials
Denisa Martonov\'a, Alain Goriely, Ellen Kuhl
https://arxiv.org/abs/2508.12063

Generalized invariants meet constitutive neural networks: A novel framework for hyperelastic materials
The major challenge in determining a hyperelastic model for a given material is the choice of invariants and the selection how the strain energy function depends functionally on these invariants. Here we introduce a new data-driven framework that simultaneously discovers appropriate invariants and constitutive models for isotropic incompressible hyperelastic materials. Our approach identifies both the most suitable invariants in a class of generalized invariants and the corresponding strain ene…

TITAN: A Trajectory-Informed Technique for Adaptive Parameter Freezing in Large-Scale VQE
Yifeng Peng, Xinyi Li, Samuel Yen-Chi Chen, Kaining Zhang, Zhiding Liang, Ying Wang, Yuxuan Du
https://arxiv.org/abs/2509.15193

TITAN: A Trajectory-Informed Technique for Adaptive Parameter Freezing in Large-Scale VQE
Variational quantum Eigensolver (VQE) is a leading candidate for harnessing quantum computers to advance quantum chemistry and materials simulations, yet its training efficiency deteriorates rapidly for large Hamiltonians. Two issues underlie this bottleneck: (i) the no-cloning theorem imposes a linear growth in circuit evaluations with the number of parameters per gradient step; and (ii) deeper circuits encounter barren plateaus (BPs), leading to exponentially increasing measurement overheads.…