End-to-end Material Thermal Conductivity Prediction through Machine
  Learning
Yagyank Srivastava Ankit Jain
We investigated the accelerated prediction of the thermal conductivity of
materials through end- to-end structure-based approaches employing machine
learning methods. Due to the non-availability of high-quality thermal
conductivity data, we first performed high-throughput calculations based on
first principles and the Boltzmann transport equation for 225 materials,
effectively more than doubling the size of the existing dataset. We assessed
the performance of state-of-the-art machine learning models for thermal
conductivity prediction on this expanded dataset and observed that all these
models suffered from overfitting. To address this issue, we introduced a novel
graph-based neural network model, which demonstrated more consistent and
regularized performance across all evaluated datasets. Nevertheless, the best
mean absolute percentage error achieved on the test dataset remained in the
range of 50-60%. This suggests that while these models are valuable for
expediting material screening, their current accuracy is still limited.