Machine learning (ML) has emerged as a powerful tool for predicting semiconductor phase stability under varying temperatures and pressures, offering a faster and more efficient alternative to traditional computational methods. By leveraging large datasets and advanced algorithms, ML models can accurately predict free energy landscapes, identify metastable phases, and accelerate high-throughput materials discovery. This article explores key ML approaches for semiconductor phase stability prediction, focusing on neural network potentials, CALPHAD modeling, and metastable phase identification, with examples from real systems like Ge-Si alloys and wurtzite-zincblende transitions.

Neural network potentials (NNPs) have become a cornerstone for free energy calculations in semiconductors. These potentials are trained on ab initio or experimental data to approximate the potential energy surface of a material, enabling efficient molecular dynamics simulations at different temperatures and pressures. A key advantage of NNPs is their ability to capture complex atomic interactions without the computational cost of density functional theory (DFT). For example, NNPs have been successfully applied to study the phase stability of silicon-germanium (Ge-Si) alloys, which exhibit phase separation at certain compositions and temperatures. By training on DFT-generated data, NNPs can predict the critical temperature for phase separation and the free energy difference between mixed and segregated states. The accuracy of NNPs depends on the quality and diversity of the training data, with recent advancements incorporating active learning to iteratively improve the model by sampling underrepresented regions of the phase space.

High-throughput CALPHAD (Calculation of Phase Diagrams) modeling has also benefited from ML techniques. CALPHAD traditionally relies on thermodynamic models fitted to experimental data, but ML can automate and optimize this process. By training models on large databases of phase diagrams and thermodynamic properties, ML algorithms can predict unknown phase boundaries and stability regions. For instance, ML-enhanced CALPHAD has been used to refine the phase diagrams of III-V semiconductors like GaAs and InP, where small changes in temperature or pressure can lead to transitions between zincblende and wurtzite structures. ML models can identify correlations between material descriptors (e.g., electronegativity, atomic radius) and phase stability, enabling predictions for new compositions without exhaustive experimental testing. Gradient boosting and random forest algorithms are particularly effective for this task due to their ability to handle nonlinear relationships in the data.

Metastable phase identification is another area where ML excels. Metastable phases, which are not the global energy minimum but persist under specific conditions, are common in semiconductors and can have unique properties. ML models can screen vast chemical spaces to identify potential metastable phases by analyzing energy landscapes and kinetic barriers. For example, ML has been used to predict metastable polymorphs of ZnO, which can form in either wurtzite or zincblende structures depending on synthesis conditions. By combining unsupervised learning for clustering similar structures and supervised learning for energy prediction, ML models can prioritize metastable phases for further experimental validation. Reinforcement learning has also been applied to explore phase transitions dynamically, simulating how external conditions drive the system between metastable states.

The Ge-Si system serves as a practical example of ML-driven phase stability analysis. Ge-Si alloys are important for optoelectronics and thermoelectrics, but their phase separation behavior complicates device fabrication. ML models trained on DFT-calculated formation energies can predict the miscibility gap and critical temperature for phase separation. These models reveal that the miscibility gap narrows under tensile strain, a finding that aligns with experimental observations. Similarly, ML has been used to study the wurtzite-zincblende transition in GaN, a material critical for high-power electronics. By analyzing the free energy difference between the two phases as a function of pressure, ML models can pinpoint the transition pressure and identify defects that stabilize one phase over the other.

Despite these successes, challenges remain in ML-based phase stability prediction. Data scarcity for certain compositions or extreme conditions can limit model accuracy, and the interpretability of ML models is often poor compared to traditional thermodynamic approaches. However, advances in transfer learning and generative models are addressing these issues. For example, generative adversarial networks (GANs) can synthesize realistic training data for rare phases, while attention mechanisms in neural networks provide insights into which features most influence phase stability.

In summary, ML methods are transforming the study of semiconductor phase stability by enabling rapid, accurate predictions across diverse conditions. Neural network potentials offer a scalable way to compute free energies, ML-enhanced CALPHAD models accelerate phase diagram construction, and metastable phase identification benefits from high-throughput screening. Real-world applications in Ge-Si alloys and wurtzite-zincblende transitions demonstrate the practical utility of these approaches. As ML techniques continue to evolve, their integration with materials science promises to unlock new semiconductor phases and optimize existing ones for advanced technologies.