Machine Learning Prediction of Heat Capacity for Solid Inorganics

@article{Kauwe2018MachineLP,
  title={Machine Learning Prediction of Heat Capacity for Solid Inorganics},
  author={Steven K. Kauwe and Jake Graser and Alfonso V{\'a}zquez and Taylor D. Sparks},
  journal={Integrating Materials and Manufacturing Innovation},
  year={2018},
  volume={7},
  pages={43-51}
}
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… 
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