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Our current understanding of melancholic depression is shaped by its position in the depression spectrum. The lack of consensus on how it should be treated—whether as a subtype of depression, or as a distinct disorder altogethe—interferes with the recovery of suffering patients. In this study, we analyzed brain state energy landscape models of melancholic depression, in contrast to healthy and non-melancholic energy landscapes. Our analyses showed significant group differences on basin energy, basin frequency, and transition dynamics in several functional brain networks such as basal ganglia, dorsal default mode, and left executive control networks. Furthermore, we found evidences suggesting the connection between energy landscape characteristics (basin characteristics) and depressive symptom scores (BDI-II and SHAPS). These results indicate that melancholic depression is distinguishable from its non-melancholic counterpart, not only in terms of depression severity, but also in brain dynamics.

The Fermi level (E F ) relative position to the conduction band minimum is a crucial consideration for controlling electrical conductivity and semiconductor device performance in thin-film transistors, sensors, and photodetectors. Experiment complexity and expensive material resources for predicting E F via an experimental approach render a machine learning (ML) approach to be more appropriate. This work presents ML-assisted E F prediction of solution-processed ultrawide-bandgap (UWB) amorphous gallium oxide (a-Ga 2 O x ). Three regression models�kernel ridge regression, support vector regression, and random forest regression�were trained with experimental features including the film thickness, baking temperature, and gas environment during solution deposition of the a-Ga 2 O x film. The results show that ML models can be used to predict E F of the UWB a-Ga 2 O x film and also identify optimized fabrication parameters to achieve the optimized E F . Moreover, the ML approach can significantly accelerate the fabrication of semiconducting UWB a-Ga 2 O x -based material for future device applications. This work is a big step toward rapid and cost-effective optimization methods for developing UWB a-Ga 2 O x -based devices.