The integration of machine learning into high-throughput screening of semiconductor materials represents a transformative shift in materials science. By leveraging computational power and advanced algorithms, researchers can rapidly analyze vast combinatorial libraries, predict key material properties, and identify promising candidates for optoelectronic, power, and other semiconductor applications. This approach significantly reduces the time and cost associated with traditional trial-and-error experimentation while uncovering novel materials with tailored properties.

High-throughput screening relies on the generation and analysis of large datasets encompassing structural, electronic, and thermodynamic properties of potential semiconductor compounds. Machine learning models process these datasets to establish correlations between composition, structure, and performance metrics such as bandgap, carrier mobility, thermal stability, and defect tolerance. Unlike conventional methods, which require extensive first-principles calculations or experimental characterization for each candidate, ML models generalize patterns from existing data to make rapid predictions for unexplored compositions.

Random forest algorithms have proven effective in classifying and predicting semiconductor properties due to their robustness against overfitting and ability to handle high-dimensional data. These ensemble methods construct multiple decision trees during training and output the average prediction, improving accuracy. For example, random forests have been employed to screen ternary and quaternary chalcogenides for photovoltaic applications, accurately predicting bandgaps and absorption coefficients across diverse chemical spaces. The interpretability of feature importance in random forests also aids in identifying dominant factors influencing material performance, such as bond lengths or electronegativity differences.

Neural networks, particularly deep learning architectures, excel in capturing nonlinear relationships within complex material datasets. Convolutional neural networks process structural representations like crystal graphs or atomic coordinates, while recurrent networks model sequential data such as time-dependent degradation or doping profiles. A notable application involved training a deep neural network on thousands of known semiconductors to predict the bandgap energies of previously untested oxide perovskites. The model achieved a mean absolute error of less than 0.3 eV compared to experimental measurements, enabling rapid identification of materials suitable for visible-light absorption.

Active learning frameworks optimize the screening process by iteratively selecting the most informative candidates for further analysis. These loops begin with an initial dataset, train a preliminary model, and then prioritize materials with high uncertainty or predicted high performance for subsequent validation. This approach minimizes the number of required experiments or simulations while maximizing discovery efficiency. In one case, an active learning cycle applied to III-V semiconductors reduced the number of density functional theory calculations by 70% while identifying three new alloys with optimal direct bandgaps for LED applications.

Several case studies demonstrate the success of ML-driven screening in semiconductor discovery. A gradient-boosted regression model analyzed over 100,000 hypothetical compositions in the Ga-In-Sn-O system, predicting electron mobility and optical transparency. Experimental validation confirmed two new amorphous oxide semiconductors with mobilities exceeding 40 cm²/Vs, suitable for next-generation transparent electronics. Another study used support vector machines to screen transition-metal-doped zinc oxide variants for spintronic applications, pinpointing specific doping configurations that enhanced room-temperature ferromagnetism.

For power electronics, machine learning has accelerated the identification of wide-bandgap semiconductors with high breakdown voltages and thermal conductivities. A kernel ridge regression model trained on binary and ternary nitrides predicted the critical electric field strengths of AlGaN alloys with 90% accuracy relative to experimental benchmarks. This led to the discovery of a previously overlooked Al-rich composition exhibiting a 40% improvement in Baliga’s figure of merit compared to conventional GaN.

Challenges remain in ensuring the reliability of ML predictions, particularly for materials lacking sufficient training data or exhibiting complex phase behavior. Techniques such as transfer learning, where models pre-trained on large inorganic databases are fine-tuned for specific semiconductor classes, help mitigate data scarcity. Uncertainty quantification methods, including Bayesian neural networks or ensemble variance analysis, provide confidence estimates for predictions, guiding experimental prioritization.

The future of ML in semiconductor screening lies in the integration of multi-fidelity data, combining high-accuracy quantum mechanical calculations with lower-cost empirical measurements. Hybrid models that embed physical constraints, such as thermodynamic stability criteria or charge neutrality conditions, further enhance predictive accuracy. As datasets grow and algorithms advance, machine learning will continue to play a pivotal role in unlocking new semiconductor materials with unprecedented performance metrics, driving innovations across electronics, energy, and sensing technologies.