Ion beam synthesized polycrystalline semiconducting FeSi2 on Si(001) has been investigated by transmission measurements at temperatures between 10 and 300 K. The existence of a minimum direct band gap was demonstrated and its variation with the temperature was studied by means of a three-parameter thermodynamic model and the Einstein model. Band tail states and states on a shallow impurity level were found to give rise to the absorption below the fundamental edge. The presence of an Urbach exponential edge was shown and the temperature dependence of the Urbach tail width was also studied based on the Einstein model. A strong structural disorder associated with grain boundaries between and within the FeSi2 grains and their related defects was found to be the dominant contribution at room temperature.

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Prediction of drug-target interactions (DTI) plays a vital role in drug development in various areas, such as virtual screening, drug repurposing and identification of potential drug side effects. Despite extensive efforts have been invested in perfecting DTI prediction, existing methods still suffer from the high sparsity of DTI datasets and the cold start problem. Here, we develop KGE_NFM, a unified framework for DTI prediction by combining knowledge graph (KG) and recommendation system. This framework firstly learns a low-dimensional representation for various entities in the KG, and then integrates the multimodal information via neural factorization machine (NFM). KGE_NFM is evaluated under three realistic scenarios, and achieves accurate and robust predictions on four benchmark datasets, especially in the scenario of the cold start for proteins. Our results indicate that KGE_NFM provides valuable insight to integrate KG and recommendation system-based techniques into a unified framework for novel DTI discovery.

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