← Back to all news
arXiv Machine Learning

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.

Related stories

arXiv Machine Learning
Sep 17

ADAPT: Lightweight, Long-Range Machine Learning Force Fields Without Graphs

The paper introduces ADAPT, a lightweight machine‑learning force field that replaces graph neural networks with a direct coordinates‑in‑space Transformer encoder to model all pairwise atomic interactions. Applied to silicon point defects, ADAPT reduces force prediction error by about 22% and energy prediction error by roughly 40% compared to a state‑of‑the‑art GNN model, while also cutting computational cost. This approach addresses common GNN issues such as oversmoothing, oversquashing, and poor long‑range interaction representation, which are especially problematic for point defect modeling.

By Evan Dramko, Yihuang Xiong, Yizhi Zhu, Geoffroy Hautier, Thomas Reps, Christopher Jermaine, Anastasios Kyrillidis
llmsefficiencybenchmarks
More like this →
arXiv AI
Jun 2

From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures

arXiv:2602. 04861v2 Announce Type: replace-cross Abstract: Machine Learning Interatomic Potentials (MLIPs) sometimes fail to reproduce the physical smoothness of the quantum potential energy surface (PES), leading to erroneous behavior in downstream simulations that standard energy and force regression evaluations can miss.

By Ryan Liu, Eric Qu, Tobias Kreiman, Samuel M. Blau, Aditi S. Krishnapriyan
llmsbenchmarks
More like this →
arXiv AI
Sep 7

A Unified Physics-Aware Quantum Machine Learning Framework across Power GaN HEMTs and Logic Nanowire FETs: Predicting Unseen Process Splits and Held-Out Geometry Combinations with Lower Error and Tighter Split-to-Split Variability

The paper introduces a reinforcement‑learning framework that automatically discovers compact parametrized quantum circuits for modeling power GaN HEMTs and logic nanowire FETs. Using a graph neural network policy trained with proximal policy optimization, the method optimizes circuit architectures based on leave‑one‑group‑out cross‑validation error, achieving the lowest mean absolute error across 11 targets compared to six classical baselines. The results show significant reductions in error and variability for key device metrics (Ioff, VTH, SS) on both HEMT and NWFET datasets, demonstrating the viability of RL‑selected quantum circuits as compact, physically consistent surrogates without explicit physical constraints.

By Rushat Rai, Yun-Yuan Wang, Autsada Kakaen, Pei-Jie Chang, Doan Viet Nguyen, Yuan-Chieh Chiu, Doldet Tantraviwat, Niall Tumilty, Simon See, Wen-Jay Lee, Tai-Yue Li, Nan-Yow Chen, Tian-Li Wu
diffusionreinforcement-learning
More like this →
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...

By Robert K. A. Bennett, Jan-Lucas Uslu, Harmon F. Gault, Asir Intisar Khan, Lauren Hoang, Tara Pe\~na, Kathryn Neilson, Young Suh Song, Zhepeng Zhang, Andrew J. Mannix, Eric Pop
More like this →
arXiv Machine Learning
Jun 2

PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials

arXiv:2601. 07742v4 Announce Type: replace-cross Abstract: Many materials properties depend on higher-order derivatives of the potential energy surface, yet machine learned interatomic potentials (MLIPs) trained with a standard loss on energy, force, and stress errors can exhibit error in curvature, degrading the prediction of vibrational properties.

By Teddy Koker, Abhijeet Gangan, Mit Kotak, Jaime Marian, Tess Smidt
fine-tuningbenchmarks
More like this →
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
More like this →
arXiv Machine Learning
Jun 2

Benchmark Dataset for Catalysis on 2D MXenes

arXiv:2606. 00794v1 Announce Type: cross Abstract: Merging first-principles calculations with machine learning (ML), we aim to accelerate the exploration of catalytic behaviour in novel materials.

By Pavlo Melnyk, Anmar Karmush, M{\aa}rten Wadenb\"ack, Ania Beatriz Rodr\'iguez-Barrera, Johanna Rosen, Michael Felsberg, Jonas Bj\"ork
benchmarks
More like this →
arXiv Machine Learning
Jul 7

SMART: A Machine Learning and Monte Carlo Framework for Rapid Analysis of Stochastic Transistor Aging and Process Variation in Digital Circuits

arXiv:2607. 05187v1 Announce Type: new Abstract: As CMOS technology scales into the deep nanometer regime, digital circuit reliability is increasingly threatened by the combined stochastic effects of Bias Temperature Instability (BTI) and Process Variation (PV).

By Arash Esshaghi, Siavash Es'haghi, Gholamreza Shahabadi, Alireza Moradi
benchmarkssafety
More like this →