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版本更新 · v2arXiv:2607.15215

Stochastic Ising networks with asymmetric and time-delayed interactions

Hantao Zhang, Sidra Gibeault, Matthew W. Daniels, Philippe Talatchian, Ursula Ebels, Advait Madhavan, Mark D. Stiles

原文摘要

Stochastic Ising networks are widely used to describe collective dynamics in complex systems and to compute, yet physical implementations of these networks often contain both asymmetric interactions and nonzero signal propagation times that fall outside conventional theories of their behavior. Here, we study stochastic Ising networks with asymmetric and time-delayed interactions motivated by experimental observations in coupled superparamagnetic tunnel junctions. Time delay fundamentally reshapes the dynamics induced by antisymmetric couplings, producing strong oscillatory temporal correlations consistent with experiment. Sufficiently long delays strongly suppress the non-uniformity of steady-state probability distributions, a behavior explained by an exponential suppression of the first-order equal-time cross-correlation between pairs of spins. Residual non-uniformity is due to the equal-time cross-correlation mediated by a third spin that contributes only at higher order in the coupling strength. These relatively featureless probability distributions coexist with pronounced delayed temporal correlations, distinguishing them from equilibrium high-temperature behavior. We further show analytically that these delay-induced near-uniform distributions emerge in a broad class of stochastic networks that lack symmetry-breaking bias fields. Introducing symmetry-breaking bias fields restores a strong dependence of the steady state on interactions with qualitatively modified behavior. Simulations of networks with five coupled spins demonstrate that these effects persist beyond minimal systems with only two spins.

physics.app-ph · cond-mat.mes-hall · cond-mat.stat-mech · cs.ET · physics.data-an

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39993

A physics-informed uncertainty-aware surrogate for $p$-$n$ junction diodes

Suchita Kulkarni

原文摘要

We develop an uncertainty-aware physics-informed surrogate for a one-dimensional $p$-$n$ junction diode, conditioned on device length, doping and bias. Using the internal state of the device as predicted by the surrogate we develop a test-suite of quantities of interest including the terminal current, depletion width, peak field and stored charge. The formalism and the quantities are can be extended to asymmetric junction and two-dimensional diodes as shown by means of benchmark analysis. For the one-dimensional symmetric junction surrogate, we obtain 0.11% median $L_1$ error on the electrostatic potential, and 1.5% and 0.61% on the electron and hole densities. Finally, we develop an uncertainty analysis which determines the validity of surrogate answer within the user-specified tolerance.

cs.LG · cond-mat.mes-hall

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2312.11206

GAnnET: A principled approach to automatically annotating charge stability diagrams

Brian Weber, Justyna P. Zwolak

原文摘要

Gate-defined semiconductor quantum-dot (QD) arrays are a promising platform for quantum computing. However, their large configuration spaces and inherent measurement noise make device tuning time consuming and difficult to scale. Although machine-learning (ML) approaches for automated tuning have proliferated rapidly, a fair comparison of methods remains limited by inconsistent metrics and a lack of standardized labeled datasets. Here, we introduce Geometry-Informed ANNotator for Electrostatic Topologies (GAnnET), a physics- and geometry-informed algorithm that automatically generates probabilistic state labels for charge stability diagrams (CSDs) of double-QD systems. GAnnET constructs local geometric models of CSD structure and combines them into a global state map with calibrated confidence, eliminating the need for manual annotation. We validate GAnnET on simulated and experimental CSDs and demonstrate consistent labeling suitable for ML training and benchmarking. By enabling reproducible annotation of experimental data, GAnnET supports community benchmarking and scalable automated QD operation.

cond-mat.mes-hall · cs.CV · quant-ph

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.28629

Mechanistic discovery in grain boundary structure and deformation driven by agentic AI

Yazhuo Liu, Kunqing Ding, Xiaoguo Gong, Zifeng Wang, Yichen Yang, Yifeng Che, Wen Chen, Josh Kacher 等 11 位作者

原文摘要

Agentic artificial intelligence (AI) systems hold immense potential for automating complex workflows in materials discovery and design, but extending these capabilities to domain-intensive mechanistic studies remains challenging. General grain boundaries (GBs) exemplify this challenge, as their structural complexity complicates experimental characterization and atomistic modeling. Here, we present GBClaw, an agentic AI framework for investigating the structure and deformation of general GBs, which are common in real polycrystals yet remain understudied. By integrating large language models into a modular architecture, GBClaw effectively combines systematic simulations with targeted mechanistic analysis through iterative workflow refinement and dynamic replanning. Applied to 14 general asymmetric tilt GBs in nickel, the framework characterized interfacial defect structures and deformation modes across these incommensurate boundaries. It established a general approach to identify previously unobserved, widely spaced secondary GB misfit dislocations (SGBMDs) alongside closely spaced primary GB misfit dislocations (PGBMDs) in simulated atomic configurations and experimental images. Comparisons of GB sliding with and without SGBMDs revealed substantial differences in shear strength, underscoring the critical role of misfit-dislocation hierarchies in sliding resistance. Researcher-guided follow-up calculations further elucidated the structural origins of shear asymmetry and an anomalous shear response. This work demonstrates that combining agentic AI with domain expertise bridges high-throughput computational screening and deep mechanistic discovery at complex material interfaces.

cond-mat.mtrl-sci · cond-mat.mes-hall · cs.AI · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39074

HERA: Assessing an Autonomous Agent for Quantum Device Measurement

Zachary W. Gomez, Nathan Bittner, Jordan T. McCourt, Qingrui Cao, Sandesh S. Kalantre, Benjamin H. Alexander, Zhiren Zheng, Kenji Watanabe 等 10 位作者

原文摘要

Large language model agents have begun to carry out laboratory measurements, but their scientific conclusions are useful only if they hold up under scrutiny. Demonstrating this requires controlled studies because models are stochastic and the context a model is given steers its conclusions. Here we introduce HERA, an autonomous agent designed to carry out cryogenic transport measurements of quantum devices. HERA draws on a knowledge base curated by lab members that holds measurement protocols, software tools, and literature references. We assessed HERA with a blinded trial against an expert on the stacking order of two multilayer graphene devices, an ablation of its knowledge base, and hundreds of offline replays in which models designed the trial's central measurement without running it. HERA's conclusions matched the expert's sealed conclusions on both tested devices. The ablation and the replay studies measured the impact of the knowledge base across different models, showing that with it every model designed better measurements and reasoned more soundly. To test how readily HERA extends to a new problem, we tasked it with finding and characterizing superconductivity in a different material system. This required a few hours of expert time to develop a new experimental protocol, after which HERA did both autonomously. These results show that, with a knowledge base designed for the task, language model agents are capable of sound experimental science.

cond-mat.mes-hall · cs.AI

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2604.24561

Remotely programming the weights of a spintronic neural network by a radiofrequency broadcast signal

M. Menshawy, D. Sanz-Hernández, L. Mazza, V. Puliafito, G. Finocchio, A. Jenkins, R. Ferreira, L. Benetti 等 10 位作者

原文摘要

Selectively programming a large number of non-volatile synaptic weights without compromising scalability is a key challenge for in-memory computing. Here, we experimentally demonstrate remote programming of synaptic weights in series-connected chains of 11 vortex-based magnetic tunnel junctions using broadcast radiofrequency signals applied through a shared strip line. Because each junction is engineered with a distinct resonance frequency, programming relies on frequency-selective reversal of the vortex-core polarity and therefore does not require individual access lines or selector devices. Reconfiguring the binary states of these chains strongly reshapes the continuous spectral transfer functions that determine the weighted sums performed on frequency-multiplexed RF inputs. This allows compact networks built from such chains to process high-dimensional RF-encoded inputs. We experimentally perform handwritten-digit classification and drone RF-signature identification using a two-chain network comprising only 22 synapses in total. After remote reconfiguration for each task, the spintronic network respectively reaches 93.9 +/- 2.3 % and 99.5 +/- 1 % test accuracy, on par with the software baseline. Broadcast RF programming thus provides a compact and scalable route to rapidly reconfigurable spintronic neuromorphic hardware.

cs.ET · cond-mat.mes-hall

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arXiv ↗PDF ↗