A Combined First-Principles and Machine Learning Study of Phase Stability and Thermoelectric Properties in ZrSiPt

Key Points

• The study aims to analyze the phase stability and thermoelectric properties of ZrSiPt using computational methods.
• Employs first-principles calculations to evaluate crystallographic stability.
• Integrates machine learning techniques to predict thermoelectric performance.
• Compares results between cubic half-Heusler and orthorhombic TiNiSi structures.
• Identifies favorable phase stability in the cubic half-Heusler structure.
• Predicts enhanced thermoelectric properties in ZrSiPt compared to other materials.
• Demonstrates the effective use of machine learning in understanding complex materials.