Specific heat and thermal conductivity variations from absolute zero to solidus temperature for standard stainless steels and aluminum alloys
Abstract
Abstract Accurate specific heat and thermal conductivity data spanning from absolute zero to the alloy’s solidus temperature are critical for designing cryogenic systems and in-space manufacturing processes. Current methodologies rely on either a low-temperature statistical thermodynamics model, which fails at elevated temperatures, or high-temperature classical thermodynamic calculations, which become unreliable below 0 °C. Therefore, a database of thermal conductivity and specific heat for standard stainless steels and aluminum alloys spanning 0 K to the alloy’s solidus temperature is currently unavailable. Here, we present a novel hybrid framework that integrates statistical thermodynamics models for low temperatures with classical thermodynamic simulations for elevated temperatures, unified through a genetic algorithm. This approach uniquely eliminates discontinuities between various temperature regimes, producing continuous thermal properties across the entire 0 K to solidus temperature range. Using this method for the first time, we have created a database of thermal conductivity and specific heat values from absolute zero to the solidus temperature for twenty-six alloys (thirteen stainless steels and thirteen aluminum alloys). The resulting datasets have been tested against literature data across different temperature regimes, with average absolute errors of 2%–5% for specific heat and 6%–16% for thermal conductivity. This novel property dataset enables reliable thermal analysis and process optimization for emerging space-based welding, additive manufacturing, and cryogenic storage applications in which materials experience extreme temperature variations.
Fields of Study & Subjects
Machine Learning in Materials Science
Solidification and crystal growth phenomena
Thermal properties of materials
Publication details
- Journal / source
- Materials Research Express
- Publisher
- IOP Publishing
- Volume
- 13
- Issue
- 8
- Pages
- 086501–086501
- Publication date
- 8 April 2026
- Language
- EN