Machine Learning Prediction of Heat Capacity for Solid Inorganics

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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Integrating Materials and Manufacturing Innovation Springer Journals

Machine Learning Prediction of Heat Capacity for Solid Inorganics

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Publisher
Springer International Publishing
Copyright
Copyright © 2018 by The Minerals, Metals & Materials Society
Subject
Materials Science; Metallic Materials; Characterization and Evaluation of Materials; Structural Materials; Surfaces and Interfaces, Thin Films; Nanotechnology
ISSN
2193-9764
eISSN
2193-9772
D.O.I.
10.1007/s40192-018-0108-9
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Integrating Materials and Manufacturing InnovationSpringer Journals

Published: May 29, 2018

References

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