Predicting phonon properties is essential for identifying thermally efficient materials. Here, an indirect bottom-up machine learning approach is able to predict comprehensive phonon properties of ~80,000 cubic crystals spanning 63 elements, thereby overcoming the computational burden of first-principles calculations.
- Alejandro Rodriguez
- Changpeng Lin
- Ming Hu
