arXiv Machine Learning
Ab initio Modeling of MoS2/Oxide Device Interfaces with Machine Learned Electronic Structures
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arXiv Machine Learning
Jun 30
MALOQ: Massively Accelerated Learning of Operators for Quantum Transport
arXiv:2606. 28911v1 Announce Type: new Abstract: Machine-learned (ML) operator models can be trained to predict density functional theory (DFT) Hamiltonian/density matrices at significantly reduced computational cost, thus extending electronic-structure calculations to previously unfeasible scales.
arXiv Machine Learning
Sep 10
Deep Learning to Automate Parameter Extraction and Model Fitting of Two-Dimensional Transistors
arXiv:2507.05134v2 Announce Type: replace Abstract: We present a deep learning approach to extract physical parameters (e.g., mobility, Schottky contact barrier height, defect profiles) of two-dimens...
arXiv Machine Learning
Sep 4
Physics-Informed Neural Network Surrogate for Oxygen Vacancy Dynamics in epitaxial $\mathrm{SrTiO_3}$ on Si memristors via Dynamic Spectral Optimization
Physics-informed neural networks (PINNs) are applied to model ion‑electronic drift‑diffusion in Pt/SrTiO₃/Si memristive heterostructures, overcoming numerical stiffness and multiscale spatial challenges. A cascaded PINN architecture with a custom Chebyshev spectral optimizer (DSO V2 Hybrid) isolates potential, carrier density, and vacancy transport into four sequential sub‑networks, avoiding condition numbers above 10¹⁶. The surrogate reproduces experimental conductive‑AFM current‑voltage hysteresis with R² > 0.96, maintains Poisson consistency, and offers differentiable inverse parameter estimation and linear‑time inference compared to conventional finite‑element solvers.
arXiv Machine Learning
Sep 15
Prescreening Point Defects in Semiconductors With Machine Learning
The paper presents physics‑guided machine‑learning models that predict defect formation energies and zero‑phonon lines (ZPLs) for point defects in semiconductors, aiming to replace costly density‑functional theory (DFT) calculations in the prescreening stage of high‑throughput workflows. Using ridge, kernel ridge, and multilayer perceptron models with three descriptors, the authors achieve mean absolute errors of 0.437 eV for formation energies and 0.202 eV for ZPLs on vacancies and substitutions in 4H‑SiC, while interstitials show larger errors (1.101 eV and 0.230 eV). These results demonstrate that the models can effectively accelerate defect screening, potentially obviating the need for expensive DFT relaxations in many cases.
arXiv AI
Jun 9
Agentic multi-fidelity learning of quasiparticle and excitonic properties
arXiv:2606. 07836v1 Announce Type: cross Abstract: Many-body GW-Bethe-Salpeter equation calculations are essential for accurate simulations of electronic structure and optical properties in modern low-dimensional nanomaterials.
arXiv Machine Learning
Sep 14
Agentic TCAD Calibration Workflow for Oxide Semiconductor Transistors
The article presents the first demonstration of an agentic TCAD calibration workflow for a fabricated bottom‑gate In–W–O transistor. Starting from measured transfer curves, the workflow uses measurement–TCAD residuals and local sensitivity tests to guide parameter corrections or additional physical models, accepting only updates that improve device metrics. For a 2%‑W reference device, five agent‑suggested updates reduced the multi‑metric device objective by 14.3× and achieved low errors in threshold voltage and on‑current across varying bias and geometry, showing model transferability and process sensitivity.