Density functional theory (DFT) is a cornerstone of semiconductor material science, enabling the prediction of electronic, optical, and mechanical properties. However, its high computational cost limits scalability, particularly for complex systems like high-entropy alloys or heavily doped semiconductors. Recent advances in artificial intelligence (AI) have introduced techniques to accelerate DFT calculations while maintaining accuracy. Key approaches include surrogate models, transfer learning for electronic structure prediction, and error-correction algorithms. These methods address the trade-off between computational efficiency and precision, enabling high-throughput screening and discovery of novel semiconductor materials.

Surrogate models replace expensive DFT calculations with fast, data-driven approximations. Machine learning (ML) models, such as neural networks or Gaussian processes, are trained on existing DFT datasets to predict material properties without solving the Kohn-Sham equations. For example, graph neural networks (GNNs) leverage atomic connectivity and local environments to predict bandgaps and formation energies with errors below 0.1 eV compared to DFT benchmarks. A study on ternary semiconductors demonstrated that surrogate models could achieve 95% accuracy in bandgap prediction while reducing computational time by three orders of magnitude. Challenges arise in systems with strong electron correlation or disordered configurations, where surrogate models may struggle without sufficient training data.

Transfer learning mitigates data scarcity by pretraining models on large, general material databases before fine-tuning on smaller semiconductor-specific datasets. For instance, a model pretrained on the Materials Project database can be adapted to predict defect energetics in silicon with fewer than 1,000 DFT calculations. This approach is particularly effective for doped systems, where the electronic structure perturbations are localized. Transfer learning reduces the need for exhaustive DFT sampling, cutting computational costs by 50-70% for defect formation energy predictions in GaN and ZnO. However, performance degrades for high-entropy alloys due to their vast compositional space and lack of representative training data.

Error-correction algorithms refine low-cost, approximate DFT results (e.g., from semi-empirical methods or small basis sets) using ML to match high-accuracy benchmarks. For example, a neural network trained on the differences between PBE and hybrid functional (HSE06) bandgaps can correct PBE predictions with mean absolute errors below 0.05 eV. This avoids the 10-100x cost of hybrid DFT while preserving accuracy. Similarly, ML-based force-field corrections enable ab initio molecular dynamics at near-classical computational cost. These methods face challenges in systems with significant spin-orbit coupling or strongly correlated electrons, where error patterns are less systematic.

Benchmark studies highlight the trade-offs between AI-accelerated and traditional DFT. In a test set of 500 binary and ternary semiconductors, surrogate models achieved bandgap predictions with a mean absolute error (MAE) of 0.12 eV versus DFT, while reducing compute time from hours to seconds per material. For doped silicon, transfer learning reduced the required DFT calculations by 80% while maintaining errors below 0.15 eV for defect levels. Error-correction methods improved PBE bandgap predictions for oxides (e.g., TiO2) from an MAE of 0.8 eV to 0.1 eV relative to HSE06, at 5% of the computational cost. However, for high-entropy alloys like (CoCrFeMnNi)Ox, AI methods currently exhibit higher errors (MAE > 0.3 eV) due to limited training data and complex electronic interactions.

Scaling AI-accelerated DFT to high-entropy systems requires addressing data sparsity and electronic complexity. One strategy is active learning, where ML models iteratively query DFT for the most informative data points, reducing the total number of calculations. In a study on quaternary semiconductors, active learning cut the required DFT runs by 60% while achieving sub-0.2 eV errors in formation energy predictions. Another approach combines surrogate models with cluster expansion techniques to handle compositional disorder, though this remains computationally intensive for systems beyond five components.

Doped semiconductors present unique challenges due to localized electronic states and low defect concentrations. ML models trained on pristine materials often fail to capture defect-induced perturbations. Hybrid approaches, where surrogate models predict bulk properties and DFT is used only for defect supercells, offer a compromise. For phosphorus-doped silicon, this hybrid workflow reduced computation time by 70% while preserving accuracy in carrier concentration predictions.

Future directions include integrating multi-fidelity datasets (combining low- and high-accuracy DFT) and developing physics-informed ML architectures that respect quantum mechanical constraints. For instance, enforcing Kohn-Sham Hamiltonian symmetries in neural networks improves transferability across material classes. Advances in scalable GNNs will also enable handling of larger supercells for disordered systems.

AI-accelerated DFT is transforming semiconductor research, enabling rapid exploration of material spaces previously deemed intractable. While challenges remain for high-entropy and strongly correlated systems, surrogate models, transfer learning, and error-correction algorithms already offer significant speedups with minimal accuracy loss. As datasets grow and algorithms improve, AI will increasingly complement traditional DFT, bridging the gap between high-throughput screening and high-fidelity simulations.