Prediction of magnetic Janus materials based on machine learning and first-principles calculations

Sepsis may lead to kidney function decline in patients with chronic kidney disease (CKD), and the deleterious effect may persist in patients who survive sepsis. We used a machine learning approach to predict the risk of end-stage renal disease (ESRD) in sepsis survivors. A total of 11,661 sepsis survivors were identified from a single-center database of 112,628 CKD patients between 2010 and 2018. During a median follow-up of 3.5 years, a total of 1366 (11.7%) sepsis survivors developed ESRD after hospital discharge. We adopted the random forest, extra trees, extreme gradient boosting, light gradient boosting machine (LGBM), and gradient boosting decision tree (GBDT) algorithms to predict the risk of ESRD development among these patients. GBDT yielded the highest area under the receiver operating characteristic curve of 0.879, followed by LGBM (0.868), and extra trees (0.865). The GBDT model revealed the strong effect of estimated glomerular filtration rates <25 mL/min/1.73 m2 at discharge in predicting ESRD development. In addition, hemoglobin and proteinuria were also essential predictors. Based on a large-scale dataset, we established a machine learning model computing the risk for ESRD occurrence among sepsis survivors with CKD. External validation is required to evaluate the generalizability of this model.

The anti-symmetric and anisotropic symmetric exchange interactions between two magnetic dipole moments – responsible for intriguing magnetic textures (e.g., magnetic skyrmions) – have been discovered since last century, while their electric analogues were either hidden for a long time or still not known. It is only recently that the anti-symmetric exchange interactions between electric dipoles was proved to exist (with materials hosting such an interaction being still rare) and the existence of anisotropic symmetric exchange interaction between electric dipoles remains ambiguous. Here, by symmetry analysis and first-principles calculations, we identify hafnia as a candidate material hosting the non-collinear dipole alignments, the analysis of which reveals the anti-symmetric and anisotropic symmetric exchange interactions between electric dipoles in this material. Our findings can hopefully deepen the current knowledge of electromagnetism in condensed matter, and imply the possibility of discovering novel states of matter (e.g., electric skyrmions) in hafnia-related materials.

Radiomics and machine learning have been extensively utilized in the realm of urinary stones, particularly in forecasting stone treatment outcomes. The objective of this study was to integrate clinical variables and radiomic features to develop a machine learning model for predicting the stone-free rate (SFR) following percutaneous nephrolithotomy (PCNL).A total of 212 eligible patients who underwent PCNL surgery at the Second Affiliated Hospital of Nanchang University were included in a retrospective analysis. Preoperative clinical variables and non-contrast-enhanced CT images of all patients were collected, and radiomic features were extracted after delineating the stone ROI. Univariate analysis was conducted to identify clinical variables strongly correlated with the stone-free rate after PCNL, and the least absolute shrinkage and selection operator algorithm (lasso regression) was utilized to screen radiomic features. Four supervised machine learning algorithms, including Logistic Regression, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Decision Tree (GBDT), were employed. The clinical variables with strong correlation and screened radiomic features were integrated into the four machine learning algorithms to construct a prediction model, and the receiver operating curve was plotted. The area under the receiver operating curve (AUC), the accuracy rate, the specificity, etc., were used to evaluate the predictive performance of the four models.After analyzing postoperative statistics, the stone-free rate following the procedure was found to be 70.3% (n = 149). Among the various clinical variables examined, factors, such as stone number, stone diameter, stone CT value, stone location, and history of stone surgery, were identified as statistically significant in relation to the stone-free rate after PCNL. A total of 121 radiomic features were extracted, and through lasso regression, 7 features most closely associated with the stone-free rate post-PCNL were identified. The predictive accuracy of different models (Logistic Regression, RF, XGBoost, and GBDT) for determining the stone-free rate after PCNL was evaluated, yielding accuracies of 78.1%, 76.6%, 75.0%, and 73.4%, respectively. The corresponding area under the curve AUC (95%CI) were 0.85 (0.83-0.89), 0.81 (0.76-0.85), 0.82 (0.78-0.85), and 0.77 (0.73-0.81), positioning these models among the top performers in logistic regression prediction. In terms of predictive importance scores, the key factors identified by the logistic regression model were number of stone, zone percentage, stone diameter, and surface area. Similarly, the RF model highlighted number of stone, stone CT value, stone diameter, and surface area as the top predictors.Among the four machine learning models, the logistic regression model demonstrated the highest accuracy and discrimination ability in predicting the stone-free rate following PCNL. In comparison to XGBoost and GBDT, RF also exhibited superior accuracy and a certain level of discrimination ability. However, based on the performance of all four models, logistic regression is more likely to aid in clinical decision-making by assisting clinicians in diagnosing PCNL in patients. This enables us to effectively predict the presence of residual stones post-surgery and ultimately select patients who are suitable candidates for PCNL.

Colorectal Cancer (CRC) is a highly prevalent malignancy globally, and early prediction is crucial for improving prognosis. This study used a multidimensional CRC dataset (n=1000) provided by the Kaggle platform, which contains 14 clinical and lifestyle characteristics. First, data imbalance was mitigated through Random Oversampling (ROM) and standardization. Subsequently, a comprehensive evaluation was performed on seven baseline machine learning models, including Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost) and so on. Based on performance metrics such as accuracy and F1 score, GBDT and XGBoost were subsequently selected as the optimal base learners. Finally, the predictive probability features generated by the base learners are fed into the meta-learners such as Random Forest (RF), K Nearest Neighbor (KNN) and Multi-Layer Perceptron (MLP) for secondary modeling. The interpretability of the model is achieved through the Shapley Additive exPlanations (SHAP) value, which quantifies the marginal contribution of each feature to the prediction. Experiments show that the RF integration architecture based on GBDT and XGBoost baseline models has the best performance (accuracy of 0.9527 and AUC of 0.9923). SHAP analysis showed that Activity_Level and BMI were core predictors with limited contribution from gender, confirming the prioritization of exercise and weight management in CRC prevention. The framework demonstrated excellent robustness and maintained its predictive advantage even when inefficient base models e.g., Logistic Regression (LR) were introduced. This study provides an interpretable machine learning paradigm for CRC risk stratification with potential for clinical translation.

Accurate prediction of in-hospital mortality for patients with severe community-acquired pneumonia (SCAP) complicated by respiratory failure admitted to the intensive care unit (ICU) remains a critical challenge. This study aimed to develop and validate a machine learning (ML) model to predict this risk and compare its performance with conventional scoring systems. In this retrospective study, data from 164 patients with SCAP and respiratory failure admitted to the ICU between January 2017 and January 2024 were analyzed. Patients were randomly divided into a training set (n = 114) and a validation (test) set (n = 50). Forty-five clinical features collected at admission were used as candidate predictors. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed for feature selection. Six ML models, including Gradient Boosting Decision Tree (GBDT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Decision Tree(DT),Support Vector Machine (SVM), and Logistic Regression(LR), were constructed and evaluated.Use SHAP analysis to assess the contribution of each feature in a machine learning model. Construct a nomogram using the top six most influential features. The GBDT model demonstrated the best predictive performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.83 (95% CI: 0.757-0.927) in the internal validation set, significantly outperforming the Acute Physiology and Chronic Health Evaluation II (APACHE-II, AUC = 0.70). Calibration curves demonstrated good agreement between predicted and observed mortality risks, particularly across the mid-probability range. Decision curve analysis indicated that the model provided a higher net benefit than "treat-all" and "treat-none" strategies across a broad range of threshold probabilities. SHapley Additive exPlanations (SHAP) analysis identified lactate, D-dimer, temperature, albumin, Prothrombin Time and Fraction of Inspired Oxygen as the six most influential predictors of in-hospital mortality. Based on these key predictors, we further developed a simplified nomogram to facilitate bedside risk estimation. The GBDT ML model, developed from routinely available clinical data, provides a highly accurate and clinically interpretable tool for predicting in-hospital mortality in SCAP patients with respiratory failure. It outperforms traditional severity scores and holds promise for assisting clinicians in risk stratification and early intervention.

Prediction of glass forming ability in amorphous alloys based on different machine learning algorithms

Background: Predicting the perioperative requirement for red blood cells (RBCs) transfusion in patients with the pelvic fracture may be challenging. In this study, we constructed a perioperative RBCs transfusion predictive model (ternary classifications) based on a machine learning algorithm.Materials and Methods: This study included perioperative adult patients with pelvic trauma hospitalized across six Chinese centers between September 2012 and June 2019. An extreme gradient boosting (XGBoost) algorithm was used to predict the need for perioperative RBCs transfusion, with data being split into training test (80%), which was subjected to 5-fold cross-validation, and test set (20%). The ability of the predictive transfusion model was compared with blood preparation based on surgeons' experience and other predictive models, including random forest, gradient boosting decision tree, K-nearest neighbor, logistic regression, and Gaussian naïve Bayes classifier models. Data of 33 patients from one of the hospitals were prospectively collected for model validation.Results: Among 510 patients, 192 (37.65%) have not received any perioperative RBCs transfusion, 127 (24.90%) received less-transfusion (RBCs < 4U), and 191 (37.45%) received more-transfusion (RBCs ≥ 4U). Machine learning-based transfusion predictive model produced the best performance with the accuracy of 83.34%, and Kappa coefficient of 0.7967 compared with other methods (blood preparation based on surgeons' experience with the accuracy of 65.94%, and Kappa coefficient of 0.5704; the random forest method with an accuracy of 82.35%, and Kappa coefficient of 0.7858; the gradient boosting decision tree with an accuracy of 79.41%, and Kappa coefficient of 0.7742; the K-nearest neighbor with an accuracy of 53.92%, and Kappa coefficient of 0.3341). In the prospective dataset, it also had a food performance with accuracy 81.82%.Conclusion: This multicenter retrospective cohort study described the construction of an accurate model that could predict perioperative RBCs transfusion in patients with pelvic fractures.

Accurately predicting thermophysical properties across various physical states is essential for both industrial and scientific applications. However, experimental data often exhibit variability and noise, requiring robust modeling approaches. In this work, we employ machine learning (ML) techniques to predict methane's thermophysical properties in liquid, vapor, and supercritical phases, including isobaric and isochoric heat capacities, density, specific volume, Joule-Thomson coefficients, enthalpies, sound speed, and shear viscosities, applying an approach recently developed [ACS Eng. Au 5, 226 (2025)10.1021/acsengineeringau.5c00001]. We explore various ML algorithms, including Adaptive Boosting, Bagging, Decision Trees, Extra Trees, Gradient Boosting, Histogram-based Gradient Boosting Regression Tree, K-Nearest Neighbors, Light Gradient Boosting Machine, Nu-Support Vector Regression, Random Forest, Extreme Gradient Boosting, and Artificial Neural Networks. The ML models, which use raw experimental data without removing outliers, produce predictions that are closer to those obtained from the equations of state (EOS) employed by the National Institute of Standards and Technology than to the same raw experimental data used as input for developing the EOS. These results highlight ML's potential to identify and generalize complex patterns, smooth inherent noise, and manage the variability of different thermophysical properties. They indicate that ML models, particularly Extra Trees and Gradient Boosting, can offer a scalable alternative for thermophysical property predictions, offering consistency and efficiency over traditional methods. Although our approach does not eliminate preprocessing, it demonstrates that ML can effectively manage noisy data independently, offering a more efficient and cost-effective alternative to conventional pre- and postprocessing techniques.

PurposeTo analyze the risk factors for preterm birth in patients with early-onset preeclampsia (EOPE) based on multi-algorithm machine learning and to construct a predictive model to explore the predictive value of the model.MethodsA retrospective analysis was conducted on 442 EOPE patients from a single tertiary center, divided into preterm birth (<37 weeks, n=358) and term-born (≥37 weeks, n=84) groups. Univariate analysis, random forest importance assessment, lasso regression combined with multivariate regression analysis were used for feature evaluation. Eight machine learning models were trained (70% data) and validated (30% data). A Stacking ensemble model was constructed, and SHapley Additive exPlanations (SHAP) was used for feature interpretation.ResultsThe area under the receiver operating characteristic curve (AUROC) for predicting preterm birth in EOPE patients using Logistic Regression, Gaussian Naive Bayes, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), Multi-Layer Perceptron, and Elastic Net were 0.763, 0.712, 0.821, 0.832, 0.821, 0.842, 0.784, and 0.763, respectively. The Stacking model (XGBoost+GBDT+SVM) achieved superior performance (AUROC=0.865). Three independent risk factors were identified: fetal growth restriction (aOR=3.50, p = 0.047), serum cystatin C (aOR=11.27, p = 0.018), and C-reactive protein (aOR=1.37, p < 0.001). SHAP analysis revealed GBDT as the top contributor to Stacking predictions, with microalbunminuria (GBDT, XGBoost) and age (SVM) being the most influential features.ConclusionMachine learning models can serve as reliable assessment tools for predicting the risk of preterm birth in patients with EOPE. The ensemble prediction model demonstrates the best predictive performance, helping obstetricians identify high-risk patients and perform early intervention to improve perinatal outcomes.

BackgroundThis study aims to develop a advanced machine learning model to predict the fall risk among community-dwelling elders. This study could present actionable advices for early prevention of fall risk.MethodsBetween October and December 2022, 977 older adults from the Hannan District of Wuhan were recruited. Data was collected using structured questionnaires. The sample was randomly split into training (732 participants) and testing (245 participants) sets at a 3:1 ratio. The primary outcome was the occurrence of fall. Five machine learning models—Random Forest (RF), Gradient Boosted Decision Tree (GBDT), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGBoost), and Categorical Features Gradient Boosting (CatBoost)—were evaluated against a Logistic Regression (LR) model. Model performance was assessed using AUC, accuracy, precision, sensitivity, specificity, and F1 score.ResultsAmong the 977 older adults, 195 experienced falls (20.0%). ROC curve analysis showed AUC values of LR, RF, LGBM, GBDT, XGBoost, and CatBoost were, respectively, 0.8390, 0.8632, 0.8614, 0.8544, 0.8705, and 0.8719. CatBoost had the highest AUC, indicating the best predictive performance. SHapley Additive exPlanations (SHAP) analysis identified key features influencing the CatBoost model: history of falls, comorbidities, polypharmacy, sleep disorders, ADL, TUG results, frailty status, and use of assistive devices.ConclusionThe fall risk prediction model for community-dwelling older adults, developed with CatBoost, showed excellent performance and can aid in early clinical assessment and fall prevention.

High flexural strength of computer-aided manufacturing resin composite blocks (CAD/CAM RCBs) are required in clinical scenarios. However, the conventional in vitro approach of modifying materials’ composition by trial and error was not efficient to explore the effective components that contribute to the flexural strength. Machine learning (ML) is a powerful tool to achieve the above goals. Therefore, the aim of this study was to develop ML models to predict the flexural strength of CAD/CAM RCBs and explore the components that affect flexural strength as the first step. The composition of 12 commercially available products and flexural strength were collected from the manufacturers and literature. The initial data consisted of 16 attributes and 12 samples. Considering that the input data for each sample were recognized as a multidimensional vector, a fluctuation range of 0.1 was proposed for each vector and the number of samples was augmented to 120. Regression algorithms—that is, random forest (RF), extra trees, gradient boosting decision tree, light gradient boosting machine, and extreme gradient boosting—were used to develop 5 ML models to predict flexural strength. An exhaustive search and feature importance analysis were conducted to analyze the effective components that affected flexural strength. The R2 values for each model were 0.947, 0.997, 0.998, 0.983, and 0.927, respectively. The relative errors of all the algorithms were within 15%. Among the high predicted flexural strength group in the exhaustive search, urethane dimethacrylate was contained in all compositions. Filler content and triethylene glycol dimethacrylate were the top 2 features predicted by all models in the feature importance analysis. ZrSiO4 was the third important feature for all models, except the RF model. The ML models established in this study successfully predicted the flexural strength of CAD/CAM RCBs and identified the effective components that affected flexural strength based on the available data set.

We computationally investigated potential piezoelectrocatalysts, two-dimensional (2D) Janus MXTe (M = Hf, Zr; X = S, Se). The structural and electronic properties, synthesis feasibility, piezoelectric properties, and hydrogen evolution reaction were calculated. Our results showed that these 2D Janus MXTe are narrow-gap semiconductors, indicating great conductivity for electrocatalysis. The feasibility of synthesis was comparable to the already synthesized Janus materials. To exhibit a piezoelectrocatalytic effect, the material has to be piezoelectric and catalytically effective simultaneously. As the Janus structure breaks the centrosymmetry, the considered MXTe are intrinsically piezoelectric. We therefore calculated the dipole moments and the variation of out-of-plane polarization upon strain. The computed piezoelectric coefficient e 31 is within the same order of magnitude as that of other Janus 2D materials. Finally, although pristine 2D Janus MXTe were inert to hydrogen evolution reaction, incorporation of single-atom defects was found to boost hydrogen adsorption significantly. The catalytic efficacy can be further tuned by biaxial tensile strain, effectively controlling the Gibbs free energy of adsorption to be close to the thermoneutral value that is indicative of an excellent hydrogen evolution reaction activity, at least for ZrSTe Janus monolayer. In summary, this work proposed and comprehensively investigated a new class of possible piezoelectrocatalysts, 2D Janus materials, which is feasible to be synthesized, catalytically effective, and has great conductivity.

ObjectiveLymphovascular invasion (LVI) is critical for the effective treatment and prognosis of breast cancer (BC). This study aimed to investigate the value of eight machine learning models based on MRI radiomic features for the preoperative prediction of LVI status in BC.MethodsA total of 454 patients with BC with known LVI status who underwent breast MRI were enrolled and randomly assigned to the training and validation sets at a ratio of 7:3. Radiomic features were extracted from T2WI and dynamic contrast-enhanced (DCE) of MRI sequences, the optimal feature filter and LASSO algorithm were used to obtain the optimal features, and eight machine learning algorithms, including LASSO, logistic regression, random forest, k-nearest neighbor (KNN), support vector machine, gradient boosting decision tree, extreme gradient boosting, and light gradient boosting machine, were used to construct models for predicating LVI status in BC. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to evaluate the performance of the models.ResultsEighteen radiomic features were retained to construct the radiomic signature. Among the eight machine learning algorithms, the KNN model demonstrated superior performance to the other models in assessing the LVI status of patients with BC, with an accuracy of 0.696 and 0.642 in training and validation sets, respectively.ConclusionThe eight machine learning models based on MRI radiomics serve as reliable indicators for identifying LVI status, and the KNN model demonstrated superior performance.This model offers substantial clinical utility, facilitating timely intervention in invasive BC and ultimately aiming to enhance patient survival rates.

The short-term reversal effect is a pervasive and persistent phenomenon in worldwide financial markets that has been found to generate abnormal returns not explainable by traditional asset pricing models. In contrast to the linear model employed in most studies on the short-term reversal, this article aims to establish a nonlinear framework to study the reversal anomaly, by using machine learning approaches. Machine learning methods including Random Forest, Adaptive Boosting, Gradient Boosted Decision Trees and extreme Gradient Boosting, are employed to test the profitability of the short-term strategy in the US and Chinese stock markets. Significant outperformances with extremely high Sharpe ratio, moderate kurtosis, and positive skewness are found, showing remarkable classification efficiency of the machine learning models and their applicability to various markets. Further studies reveal that the strategy returns can be weakened with the extension of the holding period. Notably, by comparing the performances of machine learning with our newly developed linear reversal strategy, the nonlinear methods are proved to be capable of providing a diversified model predictability with improved classification accuracy. Our research indicates the significant potential of machine learning in resolving the stock return and feature relationship, which can be helpful for quantitative traders to make profitable investment decisions.

Development of compressive strength prediction platform for concrete materials based on machine learning techniques