Prescreening Point Defects in Semiconductors With Machine Learning

arXiv:2609.14846 · cond-mat.mtrl-sci, cs.LG · Submitted 2026-09-13 · Read on arXiv

cond-mat.mtrl-sci, cs.LG

Submitted: 2026-09-13

Updated: 2026-09-13

Comments: 14 pages, 4 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Adam — 13× in this paper · explained in 2 episodes
• Loss Function — 7× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• DFT Calculations — 5× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Activation Function — 3× in this paper · explained in 1 episode
• Ab Initio — 2× in this paper · explained in 1 episode
• Automatic Defect Analysis and Qualification — 1× in this paper
• Density Functional Theory (DFT) — 1× in this paper · explained in 2 episodes
• Quantum Physics — 1× in this paper · explained in 10 episodes

Sources

• Accelerating point defect simulations using data-driven and machine learning approaches
• The Loss Surfaces of Multilayer Networks
• Review Non-convex Optimization Method for Machine Learning
• Adam: A Method for Stochastic Optimization

Related papers

• AES-Debye: an Accurate, Efficient, and Scalable Engine for Debye Scattering Calculations
• Cooperative Quantum Optical Effects of Moir'e Exciton Superlattices
• Imaging Surface Magnetization in Altermagnetic MnTe Films
• Accidental accuracy and formal consistency in GW +BSE: Exact benchmarks and regime-dependent error cancellation
• Modifying van der Waals Materials via Cavity Vacuum Fluctuations
• Linear dichroic soft X-ray microscopy of ferroelectric stripe domains in epitaxial K 0.6 Na 0.4 NbO 3