Artificial intelligence (AI) is transforming the discovery of novel semiconductor materials by enabling high-throughput screening of vast chemical spaces. A recent AI model trained on over 100,000 material datasets predicted bandgaps with an accuracy of ±0.05 eV, outperforming density functional theory (DFT) calculations by 30%. This approach has identified promising candidates like perovskite chalcogenides with tunable bandgaps between 1.2 eV and 2.8 eV for solar cell applications.
AI-driven molecular dynamics simulations are accelerating the study of material stability under extreme conditions. For instance, a neural network trained on ab initio data predicted the thermal stability of boron arsenide (BAs) up to 1500 K with an error margin of less than 5%. Such predictions are crucial for materials intended for high-temperature electronics and aerospace applications.
The integration of AI with robotic synthesis platforms has enabled rapid experimental validation of predicted materials. A fully automated system synthesized and characterized over 500 candidate materials in just six months, identifying three new semiconductors with mobilities exceeding 2000 cm²/Vs at room temperature. This represents a paradigm shift in material discovery timelines from years to months.
AI is also optimizing doping strategies to enhance material performance. A Bayesian optimization algorithm identified optimal doping concentrations for silicon-germanium (SiGe) alloys, achieving carrier lifetimes up to 10 microseconds—a record for this material system. These advancements are paving the way for next-generation optoelectronic devices.
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