Mechanical Engineering Commons^™

Rapid Prediction Of Phonon Density Of States By Graph Neural Network And High-Throughput Screening Of Candidate Substrates For Wide Bandgap Electronic Cooling, Mohammed Saif Ali Al-Fahdi

Theses and Dissertations

Machine learning has demonstrated superior performance in predicting vast materials properties. However, predicting a continuous material property such as phonon density of states (DOS) is more challenging for machine learning due to the inherent issues of data smoothing and sensitivity to peak positions. In this work, phonon DOS of 2,931 inorganic cubic structures with 63 unique elements from the Open Quantum Materials Database are calculated by high precision density functional theory (DFT). With these training data, we build an equivariant graph neural network (GNN) for total phonon DOS of crystalline materials that utilizes site positions and atomic species as input …