Machine learning approaches are transforming the study and control of point defects in semiconductors, enabling precise predictions and optimization of defect-related properties. By leveraging data-driven models, researchers can accelerate defect discovery, tailor annealing processes, and optimize doping strategies. Key techniques include graph neural networks for formation energy prediction, ML-assisted defect annealing protocols, and doping efficiency optimization. These methods are applied to technologically relevant defects such as nitrogen vacancies in diamond and DX centers in GaAs, bridging the gap between atomic-scale defect physics and industrial semiconductor engineering.

Graph neural networks (GNNs) have emerged as a powerful tool for predicting defect formation energies in semiconductors. Unlike traditional density functional theory (DFT) calculations, which are computationally expensive, GNNs leverage the inherent graph structure of crystal lattices to predict defect properties with high accuracy. The nodes in the graph represent atoms, while edges encode interatomic distances and bonding environments. For nitrogen vacancies in diamond, GNNs trained on DFT datasets achieve formation energy predictions within 0.1 eV of ab initio results while reducing computation time by several orders of magnitude. The model captures the distortion of the carbon lattice around the vacancy and the electronic states introduced by the nitrogen impurity. Similarly, for DX centers in GaAs, GNNs predict the metastable configurations and charge transition levels associated with these defects, which are critical for understanding their role in carrier trapping.

Supervised learning models also play a crucial role in defect property prediction. Kernel ridge regression and random forest algorithms trained on datasets of known defect formations energies can extrapolate to new materials or defect types. Features such as atomic radii, electronegativity, and bond lengths are used as inputs. For example, models trained on III-V semiconductors accurately predict the formation energies of antisite defects in GaN and InP, identifying trends that correlate with the ionicity of the host material. These models enable high-throughput screening of defect-prone compositions, guiding the synthesis of semiconductors with reduced intrinsic defect concentrations.

Machine learning optimizes annealing protocols to control point defect populations in semiconductors. Defect annealing is a thermally activated process, and traditional trial-and-error approaches are time-consuming. ML models trained on experimental data predict the optimal temperature and duration for annealing specific defects. In the case of nitrogen vacancies in diamond, a random forest model analyzes historical annealing data to recommend protocols that maximize vacancy conversion into nitrogen-vacancy (NV) centers, which are critical for quantum sensing applications. The model considers parameters such as initial nitrogen concentration, annealing atmosphere, and heating rate, achieving a 20% improvement in NV center yield compared to empirical methods.

Reinforcement learning (RL) further refines annealing strategies by iteratively improving process parameters. An RL agent interacts with a simulated annealing environment, receiving rewards for reducing defect concentrations or enhancing desired defect configurations. For silicon wafers, RL has been used to optimize annealing schedules that minimize vacancy clusters while preserving dopant activation. The agent explores temperature ramps and quenching rates, converging on protocols that reduce post-anneal defect densities by over 30% compared to industry standards.

Doping efficiency optimization is another area where ML provides significant advantages. The interplay between dopants and native defects often limits carrier concentrations in semiconductors. ML models predict the most effective dopant-defect complexes and their electronic impact. In GaAs, for instance, support vector machines classify DX centers based on their local atomic environments, identifying dopant species such as Si or Te that minimize DX center formation. The models reveal that certain dopant-site preferences suppress the metastable states responsible for carrier trapping, leading to higher electron mobility.

Bayesian optimization is employed to tune doping concentrations and co-doping schemes. By modeling the relationship between doping parameters and electrical properties, the algorithm iteratively suggests optimal doping levels. In ZnO, Bayesian optimization identifies Mg co-doping ratios that reduce oxygen vacancy concentrations while maintaining high carrier mobility. The approach reduces the number of experimental iterations needed to achieve target resistivity values by a factor of five.

ML also aids in defect characterization by interpreting experimental data. Neural networks analyze scanning transmission electron microscopy (STEM) images to identify vacancy clusters or interstitials with sub-angstrom precision. In silicon carbide, convolutional neural networks (CNNs) classify dislocation types and their associated point defect clouds, enabling targeted defect engineering. Similarly, ML models process photoluminescence spectra to quantify defect densities in GaN, correlating emission line shapes with specific defect configurations.

The integration of ML with ab initio methods enhances defect simulations. Active learning algorithms select the most informative DFT calculations to perform, reducing the computational cost of mapping defect energy landscapes. For example, in studying sulfur vacancies in MoS2, active learning identifies the critical configurations needed to accurately model the vacancy's electronic structure, cutting the required DFT calculations by 70%.

Challenges remain in ensuring the transferability of ML models across different semiconductor systems and in acquiring high-quality training data. However, the continued development of robust descriptors and hybrid physics-ML models promises to further improve defect prediction and control. As these techniques mature, they will enable the design of semiconductors with tailored defect properties for applications ranging from quantum technologies to high-power electronics. The combination of graph-based representations, automated annealing optimization, and doping efficiency tools positions ML as an indispensable asset in semiconductor defect engineering.