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One of the most challenging problems in condensed matter physics is to predict crystal structure just from the chemical formula of the material. In this work, we present a robust machine learning (ML) predictor for the crystal point group of ternary materials (A$$_l$$ l B$$_m$$ m C$$_n$$ n ) - as first step to predict the structure - with very small set of ionic and positional fundamental features. From ML perspective, the problem is strenuous due to multi-labelity, multi-class, and data imbalance. The resulted prediction is very reliable as high balanced accuracies are obtained by different ML methods. Many similarity-based approaches resulted in a balanced accuracy above 95% indicating that the physics is well captured by the reduced set of features; namely, stoichiometry, ionic radii, ionization energies, and oxidation states for each of the three elements in the ternary compound. The accuracy is not limited by the approach; but rather by the limited data points and we should expect higher accuracy prediction by having more reliable data.

Imbalanced data sets in materials informatics are pervasive and pose a challenge to the development of classification models. This work investigates crystal point group prediction as an example of an imbalanced classification problem in materials informatics. Multiple resampling and classification techniques were considered. The findings suggest that the most influential variable of the resampling algorithms is the one controlling the number of samples to omit (undersample) or synthetically generate (oversample), as expected. The effect of balancing is to enhance the classification performance of the minority class at the cost of reducing the correct predictions of the majority class. Moreover, ideal balancing, where the classes are precisely balanced, is not optimum. Alternatively, partial balancing should be performed. In this study, the ideal ratio of the minority to majority class was found to be around two-thirds. The biggest improvement in the classification was for the random undersampling technique with k-nearest neighbors and random forest.

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Background: Acute Respiratory Distress Syndrome (ARDS) is caused by non-cardiogenic pulmonary edema and occurs in critically ill patients. It is one of the fatal complications observed among severe COVID-19 cases managed in intensive care units (ICU). Supportive lung-protective ventilation and prone positioning remain the mainstay interventions. Purpose: We describe the severity of ARDS, clinical outcomes, and management of ICU patients with laboratory-confirmed COVID-19 infection in multiple Saudi hospitals. Methods: A multicenter retrospective cohort study was conducted of critically ill patients who were admitted to the ICU with COVID-19 and developed ARDS. Results: During our study, 1154 patients experienced ARDS: 591 (51.2%) with severe, 415 (36.0%) with moderate, and 148 (12.8%) with mild ARDS. The mean sequential organ failure assessment (SOFA) score was significantly higher in severe ARDS with COVID-19 (6 ± 5, p = 0.006). Kaplan–Meier survival analysis showed COVID-19 patients with mild ARDS had a significantly higher survival rate compared to COVID-19 patients who experienced severe ARDS (p = 0.023). Conclusion: ARDS is a challenging condition complicating COVID-19 infection. It carries significant morbidity and results in elevated mortality. ARDS requires protective mechanical ventilation and other critical care supportive measures. The severity of ARDS is associated significantly with the rate of death among the patients.