Predicting miscibility in binary compounds: a machine learning and genetic algorithm study.

The combination of data science and materials informatics has significantly propelled the advancement of multi-component compound synthesis research. This study employs atomic-level data to predict miscibility in binary compounds using machine learning, demonstrating the feasibility of such predictions. We have integrated experimental data from the Materials Project (MP) database and the Inorganic Crystal Structure Database (ICSD), covering 2346 binary systems.

Hemoglobin is associated with BMDs and risk of the 10-year probability of fractures in patients with type 2 diabetes mellitus.

Purpose: This study aimed to investigate the associations between hemoglobin (HGB) levels and bone mineral density (BMD) and fracture risk in type 2 diabetes mellitus(T2DM) population of different ages.

Method: This cross-sectional study included 641 patients with T2DM (57.9% males).