arXiv subjects

Sandy A. Ekahana

Publications and source records attributed to Sandy A. Ekahana.

3 recordsLinked to original sources

arXiv2026

Framework for an AI-Ready ARPES Community Database

Condensed matter stands to gain profoundly from widespread adoption of artificial intelligence (AI). However, its current means of data organization impedes this endeavor. As a premier measure of electronic structure underlying material properties, Angle-Resolved Photoemission Spectroscopy (ARPES) has accumulated over four decades of spectra, yet the community's publication-centered workflow preserves only a highly selected fraction of its experimental record. A shared data ecosystem could transform these fragmented measurements and their contexts into a persistent, collective scientific memory, creating immediate value for reproducibility and reuse while laying the foundation for AI-enabled discovery. In this Perspective, we report outcomes of a recent community workshop and propose a practical path toward such an ecosystem. Its benefits unfold along three timescales. In the near term, standardized, searchable, and citable datasets strengthen analysis pipelines independently of AI. In the intermediate future, community-validated tools operating across laboratories could enable broader reuse and increasingly closed-loop automated experiments. In the long term, a diverse corpus of measured electronic spectra could support foundation models for quantum matter. Looking forward, a diverse corpus of measured electronic spectra could support foundation models for quantum matter and new discoveries. We discuss lessons from existing scientific databases, the current state of AI in ARPES, as well as areas of community convergence on data organization, access, credit, governance, and a staged pilot program.

cond-mat.mtrl-sci↗

arXiv2026

Electrically tunable interfacial thermal conduction via electronic structure engineering in ${Au}$/$Bi_{1-x}$$Sb_{x}$ topological insulators

This work provides direct experimental evidence for the role of topological interface states in thermal conduction across a metal/topological insulator junction. It also shows that this conduction can be reversibly modulated by electrical current injection, offering a new approach toward active control of heat flow at solid-state interfaces. Specifically, the interfacial thermal conductance of ${Au}$/$Bi_{89}$$Sb_{11}$ and ${Au}$/$Bi_{87}$$Sb_{13}$ junctions demonstrates distinct temperature- and bias-dependent behavior. Both responses are attributed to carrier redistribution between topological interface and bulk band states, driven thermally by Fermi-Dirac broadening and electrically by quasi-Fermi-level shifts and WKB tunneling into nearby bulk bands. Control experiments using trivial semimetals and insulating interlayers further confirm the topological specificity of the effect. Such electrically tunable interfacial heat conduction positions interface electronic structure engineering as a promising route for active thermal management. In doing so, it lays the groundwork for a mechanically robust alternative to conventional structure-driven thermal control compatible with increasingly dense, high-power solid-state devices.

cond-mat.mes-hall↗

arXiv2020

Super Resolution Convolutional Neural Network for Feature Extraction in Spectroscopic Data

Two dimensional (2D) peak finding is a common practice in data analysis for physics experiments, which is typically achieved by computing the local derivatives. However, this method is inherently unstable when the local landscape is complicated, or the signal-to-noise ratio of the data is low. In this work, we propose a new method in which the peak tracking task is formalized as an inverse problem, thus can be solved with a convolutional neural network (CNN). In addition, we show that the underlying physics principle of the experiments can be used to generate the training data. By generalizing the trained neural network on real experimental data, we show that the CNN method can achieve comparable or better results than traditional derivative based methods. This approach can be further generalized in different physics experiments when the physical process is known.

physics.data-an↗