Hyperparameter optimization and force error correction of neuroevolution potential for predicting thermal conductivity of wurtzite GaN

【摘要】 As a representative of wide-bandgap semiconductors, wurtzite gallium nitride(GaN) has been widely utilized in high-power devices due to its high breakdown voltage and low specific on-resistance. Accurate prediction of wurtzite GaN’s thermal conductivity is a prerequisite for designing effective thermal management systems for electronic applications. Machine learning-driven molecular dynamics simulation offers a promising approach to predicting the thermal conductivity of large-scale systems without requiring predefined parameters. However, these methods often underestimate the thermal conductivity of materials with inherently high thermal conductivity due to the large predicted force error compared with first-principles calculations, posing a critical challenge for their broader application. In this study, we successfully developed a neuroevolution potential for wurtzite GaN and accurately predicted its thermal conductivity, 259±6 W/(m·K)at room temperature, achieving excellent agreement with reported experimental measurements. The hyperparameters of the neuroevolution potential(NEP) were optimized based on a systematic analysis of reproduced energy and force, structural features, and computational efficiency. Furthermore, a force error correction method was implemented, effectively reducing the error caused by the additional force noise in the Langevin thermostat by extrapolating to the zero-force error limit. This study provides valuable insights and holds significant implications for advancing efficient thermal management technologies in wide-bandgap semiconductor devices.更多还原

【Abstract】 As a representative of wide-bandgap semiconductors, wurtzite gallium nitride(GaN) has been widely utilized in high-power devices due to its high breakdown voltage and low specific on-resistance. Accurate prediction of wurtzite GaN’s thermal conductivity is a prerequisite for designing effective thermal management systems for electronic applications. Machine learning-driven molecular dynamics simulation offers a promising approach to predicting the thermal conductivity of large-scale systems without requiring predefined parameters. However, these methods often underestimate the thermal conductivity of materials with inherently high thermal conductivity due to the large predicted force error compared with first-principles calculations, posing a critical challenge for their broader application. In this study, we successfully developed a neuroevolution potential for wurtzite GaN and accurately predicted its thermal conductivity, 259±6 W/(m·K)at room temperature, achieving excellent agreement with reported experimental measurements. The hyperparameters of the neuroevolution potential(NEP) were optimized based on a systematic analysis of reproduced energy and force, structural features, and computational efficiency. Furthermore, a force error correction method was implemented, effectively reducing the error caused by the additional force noise in the Langevin thermostat by extrapolating to the zero-force error limit. This study provides valuable insights and holds significant implications for advancing efficient thermal management technologies in wide-bandgap semiconductor devices.更多还原