# Simulating lattice thermal conductivity in semiconducting materials using high-dimensional neural network potential

## TL;DR

This paper introduces a high-dimensional neural network potential that accurately predicts lattice thermal conductivity in semiconductors, matching DFT results and enabling efficient simulations for materials like Si and GaN.

## Contribution

The study develops and validates a high-dimensional neural network potential that achieves DFT-level accuracy in predicting thermal conductivity of semiconductors.

## Key findings

- HDNNP predicts thermal conductivity within 1% of DFT for Si.
- HDNNP predicts thermal conductivity within 5.4% of DFT for GaN.
- Root mean square error of forces is less than 40 meV/Å.

## Abstract

We demonstrate that a high-dimensional neural network potential (HDNNP) can predict the lattice thermal conductivity of semiconducting materials with an accuracy comparable to that of density functional theory (DFT) calculation. After a training procedure based on the force, the root mean square error between the forces predicted by the HDNNP and DFT is less than 40 meV/{\AA}. As typical examples, we present the results for Si and GaN bulk crystals. The deviation from the thermal conductivity calculated using DFT is within 1% at 200 to 500 K for Si and within 5.4% at 200 to 1000 K for GaN.

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Source: https://tomesphere.com/paper/1905.08508