Thermal Transport in SrSnO 3 Revealed by First-principles Theory, Raman Thermometry and Machine Learning

Key Points

• This research aims to investigate the thermal transport properties of SrSnO3 using advanced methodologies.
• Utilized Raman thermometry and laser flash analysis (LFA) for thermal conductivity measurements.
• Employed first-principles calculations based on density functional theory (DFT).
• Leveraged machine learning techniques to analyze the data and improve predictions.
• Demonstrated significant variations in thermal conductivity across different conditions.
• Identified correlations between experimental data and DFT calculations.
• Machine learning analysis improved prediction accuracy for thermal transport properties.