Machine Learning Prediction of Heat Capacity for Solid Inorganics
Many thermodynamic calculations and engineering applications require the temperature-dependent heat capacity (Cp) of a material to be known a priori. First-principle calculations of heat capacities can stand in place of experimental information, but these calculations are costly and expensive. Here, we report on our creation of a high-throughput supervised machine learning-based tool to predict temperature-dependent heat capacity. We demonstrate that material heat capacity can be correlated to a number of elemental and atomic properties. The machine learning method predicts heat capacity for thousands of compounds in seconds, suggesting facile implementation into integrated computational materials engineering (ICME) processes. In this context, we consider its use to replace Neumann-Kopp predictions as a high-throughput screening tool to help identify new materials as candidates for engineering processes. Also promising is the enhanced speed and performance compared to cation/anion contribution methods at elevated temperatures as well as the ability to improve future predictions as more data are made available. This machine learning method only requires formula inputs when calculating heat capacity and can be completely automated. This is an improvement to common best-practice methods such as cation/anion contributions or mixed-oxide approaches which are limited in application to specific materials and require case-by-case considerations.
KeywordsNeumann-Kopp Heat capacity Inorganic solids Machine learning Material design High-throughput
The authors gratefully acknowledge support from the NSF CAREER Award DMR 1651668.
- 2.Barin I, Platzki G (1989) Thermochemical data of pure substances. vol 304, Wiley Online LibraryGoogle Scholar
- 6.Graser J, Kauwe SK, Sparks TD (2017) Machine learning and energy minimization approaches for crystal structure predictions: A review and new horizons, Chemistry of Materials, in pressGoogle Scholar
- 8.Kittel C (2005) Introduction to solid state physics. Wiley, HobokenGoogle Scholar
- 13.Oliynyk A, Mar A Discovery of intermetallic compounds from traditional to machine-learning approaches. Accounts of chemical research. https://doi.org/10.1021/acs.accounts.7b00490
- 15.Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: Machine learning in Python. J Mach Learn Res 12:2825–2830Google Scholar
- 20.Thermart: Freed-thermodynamic database (2017). http://www.thermart.net/freed-thermodynamic-database/