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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
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 …
Characterizationn Of Polumer-Based Mems Pyroelectic Infrared Detector, Mark E. Allard
Characterizationn Of Polumer-Based Mems Pyroelectic Infrared Detector, Mark E. Allard
Theses and Dissertations
AFRL/MLPJE had developed a novel thermal sensing material termed protein-impregnated-polymer (PIP). Thus far, a proof-of-concept has been demonstrated using a macro-sized pixel (0.64 mm2) as a bolometric detector. In an effort to better characterize this novel thermal sensing material, experimental data was used to determine figures of merit (FOMs) comparative to off-the-shelf thermal detectors. Microelectromechanical (MEMS) pixels were designed and used as the support structure for an inkjet-deposited droplet of the PIP. During the material characterization, two observations were made: PIP is a pyroelectric material, and the polymer (polyvinyl alcohol (PVA)) without the protein was found to be …