Discovering two-dimensional magnetic topological insulators by machine learning

Topological materials with unconventional electronic properties have been investigated intensively for both fundamental and practical interests. Thousands of topological materials have been identified by symmetry-based analysis and ab initio calculations. However, the predicted magnetic topological insulators with genuine full band gaps are rare. Here we employ this database and supervisedly train neural networks to develop a heuristic chemical rule for electronic topology diagnosis. The learned rule is interpretable and diagnoses with a high accuracy whether a material is topological using only its chemical formula and Hubbard $U$ parameter. We next evaluate the model performance in several different regimes of materials. Finally, we integrate machine-learned rule with ab initio calculations to high-throughput screen for magnetic topological insulators in 2D material database. We discover 6 new classes (15 materials) of Chern insulators, among which 4 classes (7 materials) have full band gaps and may motivate for experimental observation. We anticipate the machine-learned rule here can be used as a guiding principle for inverse design and discovery of new topological materials.Comment: 7 pages, 4 figure

A-Optimal designs for mixture polynomial models with heteroscedastic errors

This paper searches $A$-optimal designs for mixture polynomial models when the errors are heteroscedastic. Sufficient conditions are given so that $A$-optimal designs for the complex mixture polynomial models can be constructed from the direct sum of $A$-optimal designs for their sub-mixture models with different structures of heteroscedasticity. Several examples are presented to further illustrate and check optimal designs based on $A$-optimality criterion

A clinical prediction model based on interpretable machine learning algorithms for prolonged hospital stay in acute ischemic stroke patients: a real-world study

ObjectiveAcute ischemic stroke (AIS) brings an increasingly heavier economic burden nowadays. Prolonged length of stay (LOS) is a vital factor in healthcare expenditures. The aim of this study was to predict prolonged LOS in AIS patients based on an interpretable machine learning algorithm.MethodsWe enrolled AIS patients in our hospital from August 2017 to July 2019, and divided them into the “prolonged LOS” group and the “no prolonged LOS” group. Prolonged LOS was defined as hospitalization for more than 7 days. The least absolute shrinkage and selection operator (LASSO) regression was applied to reduce the dimensionality of the data. We compared the predictive capacity of extended LOS in eight different machine learning algorithms. SHapley Additive exPlanations (SHAP) values were used to interpret the outcome, and the most optimal model was assessed by discrimination, calibration, and clinical utility.ResultsProlonged LOS developed in 149 (22.0%) of the 677 eligible patients. In eight machine learning algorithms, prolonged LOS was best predicted by the Gaussian naive Bayes (GNB) model, which had a striking area under the curve (AUC) of 0.878 ± 0.007 in the training set and 0.857 ± 0.039 in the validation set. The variables sorted by the gap values showed that the strongest predictors were pneumonia, dysphagia, thrombectomy, and stroke severity. High net benefits were observed at 0%–76% threshold probabilities, while good agreement was found between the observed and predicted probabilities.ConclusionsThe model using the GNB algorithm proved excellent for predicting prolonged LOS in AIS patients. This simple model of prolonged hospitalization could help adjust policies and better utilize resources

Construction of Full Order-of-Addition Generalization Simplex-Centroid Designs by the Directed Graph Approach

The order-of-addition generalization simplex-centroid designs play a key role in mixture experiments, for example, the mixture experiments with process variables. The paper formally combines order-of-addition (OofA) with mixture components in the same experiment. This paper proposes a new algorithm which generates full OofA mth-degree generalization simplex-centroid designs for q components by a class of the direction graphs, and a series of examples also confirms the value of the proposed algorithm

Construction of Full Order-of-Addition Generalization Simplex-Centroid Designs by the Directed Graph Approach

The order-of-addition generalization simplex-centroid designs play a key role in mixture experiments, for example, the mixture experiments with process variables. The paper formally combines order-of-addition (OofA) with mixture components in the same experiment. This paper proposes a new algorithm which generates full OofA mth-degree generalization simplex-centroid designs for q components by a class of the direction graphs, and a series of examples also confirms the value of the proposed algorithm

“Fluid bearing” effect of enclosed liquids in grooves on drag reduction in microchannels

We report details of the fluid motion formed within and above grooves when a laminar continuous phase fluid flows over a second immiscible fluid enclosed in a grooved microchannel. Vortical structures within the transverse grooves were caused by a slip velocity at the fluid-fluid interface and act as “fluid bearings” on the boundary to lubricate the flow of the continuous phase. We investigated the drag reduction in the laminar flow in the microchannel by measuring slip at the boundaries and calculating an effective slip length, taking into account the influence of the effect of the viscosity ratio of the two fluids on the effective slip length. The “fluid bearing” effect can be used to transport high viscosity fluids using low viscosity fluids trapped in cavities to reduce drag