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Nonlinear optical crystals (NLO) are a key class of functional materials in the field of laser technology due to their excellent frequency conversion effects and physical–chemical stability. The research aims to find NLO crystals with superior stability by predicting their formation energy. In this study, only compositional information is utilized as input features and models are constructed using regression algorithms such as Random Forest Regression (RFR), Support Vector Regression (SVR), and Gradient Boosting Regression (GBR). Notably, the GBR model exhibited outstanding predictive performance, with an R2 value of 0.935 and root mean square error (RMSE) of 0.248 eV per atom. Additionally, SHapley Additive exPlanations (SHAP) analysis is employed to elucidate the fundamental principles behind the predictions by assessing the contribution of each feature to the formation energy. To validate the reliability of the models, first‐principles calculations are conducted to predict the formation energy of materials of GaP, ZnGeP2, and CdSiP2. The error range between the model predictions and the Generalized Gradient Approximation (GGA) calculated values is ≈0.1 eV per atom, confirming the accuracy of the models. [ABSTRACT FROM AUTHOR]

In modern laser science, nonlinear optical crystals play a crucial role as indispensable functional materials. Overcoming the inherent defects of traditional materials such as two-photon absorption and low laser damage thresholds, the search for high-performance nonlinear optical crystals remains an urgent and unresolved challenge. In this study, we extensively utilized deep learning techniques to construct a lightweight convolutional neural network. This network is built on the basis of information about the nuclear charge of elements and the number of atoms in compounds, creating carefully designed feature maps for accurately predicting the formation energy of nonlinear optical crystals. Our model demonstrates outstanding performance, with predictive metrics showing R2 = 0.985, RMSE = 0.128, and MAE = 0.083. This remarkable predictive capability enables our network to efficiently predict the formation energy of crystals and reliably assess their stability. Our approach is poised to play a crucial role in high-throughput screening, significantly reducing computational burdens and accelerating the discovery process of novel crystals. This significant achievement provides an efficient and reliable tool for crystal material research, opening up new possibilities for future materials science investigations. [ABSTRACT FROM AUTHOR]

Background/aim: We aimed to construct a validated nomogram model for predicting short-term (28-day) ischemic stroke mortality among critically ill populations. Materials and methods: We collected raw data from the Medical Information Mart for Intensive Care IV database, a comprehensive repository renowned for its depth and breadth in critical care information. Subsequently, a rigorous analytical framework was employed, incorporating a 10-fold cross-validation procedure to ensure robustness and reliability. Leveraging advanced statistical methodologies, specifically the least absolute shrinkage and selection operator regression, variables pertinent to 28-day mortality in ischemic stroke were meticulously screened. Next, binary logistic regression was utilized to establish nomogram, then applied concordance index to evaluate discrimination of the prediction models. Predictive performance of the nomogram was assessed by integrated discrimination improvement (IDI) and net reclassification index (NRI). Additionally, we generated calibration curves to assess calibrating ability. Finally, we evaluated the nomogram's net clinical benefit using decision curve analysis (DCA), in comparison with scoring systems clinically applied under common conditions. Results: A total of 2089 individuals were identified and assigned into training (n = 1443) or validation (n = 646) cohorts. Various identified risk factors, including age, ethnicity, marital status, underlying metastatic solid tumor, Charlson comorbidity index, heart rate, Glasgow coma scale, glucose concentrations, white blood cells, sodium concentrations, potassium concentrations, mechanical ventilation, use of heparin and mannitol, were associated with short-term (28-day) mortality in ischemic stroke individuals. A concordance index of 0.834 was obtained in the training dataset, indicating that our nomogram had good discriminating ability. Results of IDI and NRI in both cohorts proved that our nomogram had positive improvement of predictive performance, compared to other scoring systems. The actual and predicted incidence of mortality showed favorable concordance on calibration curves (P > 0.05). DCA curves revealed that, compared with scoring systems clinically used under common conditions, the constructed nomogram yielded a greater net clinical benefit. Conclusions: Utilizing a comprehensive array of fourteen readily accessible variables, a prognostic nomogram was meticulously formulated and rigorously validated to provide precise prognostication of short-term mortality within the ischemic stroke cohort. [ABSTRACT FROM AUTHOR]