Accelerated Design for High-Entropy Alloys Based on Machine Learning and Multiobjective Optimization.

High-entropy alloys (HEAs) with high hardness and high ductility can be considered as candidates for wear-resistant applications. However, designing novel HEAs with multiple desired properties using traditional alloy design methods remains challenging due to the enormous composition space. In this work, we proposed a machine-learning-based framework to design HEAs with high Vickers hardness (H) and high compressive fracture strain (D). Initially, we constructed data sets containing 172,467 data with 161 features for D and H, respectively. Four-step feature selection was performed, with the selection of 12 and 8 features for the D and H prediction models based on the optimal algorithms of the support vector machine (SVR) and light gradient boosting machine (LightGBM), respectively. The R2 of the well-trained models reached 0.76 and 0.90 for the 10-fold cross validation. Nondominated sorting genetic algorithm version II (NSGA-II) and virtual screening were employed to search for the optimal alloying compositions, and four recommended candidates were synthesized to validate our methods. Notably, the D of three candidates have shown significant improvements compared to the samples with similar H in the original data sets, with increases of 135.8, 282.4, and 194.1% respectively. Analyzing the candidates, we have recommended suitable atomic percentage ranges for elements such as Al (2-14.8 at %), Nb (4-25 at %), and Mo (3-9.9 at %) in order to design HEAs with high hardness and ductility.

Feature Selection in Machine Learning for Perovskite Materials Design and Discovery.

Perovskite materials have been one of the most important research objects in materials science due to their excellent photoelectric properties as well as correspondingly complex structures. Machine learning (ML) methods have been playing an important role in the design and discovery of perovskite materials, while feature selection as a dimensionality reduction method has occupied a crucial position in the ML workflow. In this review, we introduced the recent advances in the applications of feature selection in perovskite materials. First, the development tendency of publications about ML in perovskite materials was analyzed, and the ML workflow for materials was summarized. Then the commonly used feature selection methods were briefly introduced, and the applications of feature selection in inorganic perovskites, hybrid organic-inorganic perovskites (HOIPs), and double perovskites (DPs) were reviewed. Finally, we put forward some directions for the future development of feature selection in machine learning for perovskite material design.

Discovery of ANO1 Inhibitors based on Machine learning and molecule docking simulation approaches.

Calcium-activated chloride channels (CaCCs) are chloride channels that are regulated according to intracellular calcium ion concentrations. The channel protein ANO1 is widely present in cells and is involved in physiological activities including cellular secretion, signaling, cell proliferation and vasoconstriction and diastole. In this study, the ANO1 inhibitors were investigated with machine learning and molecular simulation. Two-dimensional structure-activity relationship (2D-SAR) and three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed for the qualitative and quantitative prediction of ANO1 inhibitors. The results showed that the prediction accuracies of the model were 85.9% and 87.8% for the training and test sets, respectively, and 85.9% and 87.8% for the rotating forest (RF) in the 2D-SAR model. The CoMFA and CoMSIA methods were then used for 3D QSAR modeling of ANO1 inhibitors, respectively. The q2 coefficients for model cross-validation were all greater than 0.5, implying that we were able to obtain a stable model for drug activity prediction. Molecular docking was further used to simulate the interactions between the five most promising compounds predicted by the model and the ANO1 protein. The total score for the docking results between all five compounds and the target protein was greater than 6, indicating that they interacted strongly in the form of hydrogen bonds. Finally, simulations of amino acid mutations around the docking cavity of the target proteins showed that each molecule had two or more sites of reduced affinity following a single mutation, indicating outstanding specificity of the screened drug molecules and their protein ligands.

Disaggregated impacts of off-farm work participation on household vulnerability to food poverty in Ghana.

This study examines disaggregated impacts of participation in off-farm employment on household vulnerability to food poverty in Ghana. We use household-level data collected from smallholder farmers in Ghana. This study employs the multinomial endogenous switching regression model to account for selection bias due to both observed and unobserved heterogeneity. Our results indicate that participation in off-farm employment activities, such as petty trading, significantly decreases household vulnerability to food poverty. Our findings further show that households that do participate in arts and crafts as an off-farm activity are more vulnerable to food poverty had they not participated. This paper provides useful policy insights to enable smallholders involved in off-farm work activities to improve food consumption expenditure and reduce their risk of food poverty.

Accelerated Discovery of Ternary Gold Alloy Materials with Low Resistivity via an Interpretable Machine Learning Strategy.

New ternary gold alloys with low resistivities (ρ) were screened out via an interpretable machine learning strategy by using the support vector regression (SVR) model integrated with SHAP analysis. The correlation coefficient (R) and the root mean square error (RMSE) of test set were 0.876 and 0.302, respectively, indicating the strong generalization ability of the model. The average ρ of top 10 candidates was 1.22×10-7 â€…Ω m, which was 41% lower than the known minimum of 2.08×10-7 â€…Ω m. The outputs of SVR model were analyzed with the critical SHAP values including first ionization energy of C-site (584 kJ ⋅ mol-1 ), electronegativity of C-site (1.72) and the second ionization energy of B-site (1135 kJ ⋅ mol-1 ), respectively. Moreover, an online web server was developed to share the model at http://materials-data-mining.com/onlineservers/wxdaualloy.

Inverse Design of Hybrid Organic-Inorganic Perovskites with Suitable Bandgaps via Proactive Searching Progress.

Hybrid organic-inorganic perovskites (HOIPs) have shown the encouraging development in solar cells that have achieved excellent device performance. One of the most important issues has been focused on finding Pb-free candidates with suitable bandgaps, which could accelerate the commercialization of environmentally friendly HOIP-based cells. Herein, we propose a new inverse design method, proactive searching progress (PSP), to efficiently discover potential HOIPs from universal chemical space by combining machine learning (ML) techniques. Compared to the pioneering work on this topic, we carried out our ML study based on 1201 collected HOIP samples with experimental bandgaps rather than theoretical properties. On the basis of 25 selected features, a weighted voting regressor ML model was constructed to predict bandgaps of HOIPs. The model comprehensively embedded four submodels and performed the coefficient determinations of 0.95 for leaving-one-out cross-validation and 0.91 for testing set. The feature analysis revealed that the tolerance factor (t f) below 0.971 and the new tolerance factor (τf) in 3.75-4.09 contributed to lower bandgaps and vice versa. By applying the PSP method, the Pb-free HOIPs with optimal bandgaps were successfully designed from a generated chemical space comprising over 8.20 × 1018 combinations, which included 733848 candidates (e.g., Cs0.334FA0.266MA0.400Sn0.769Ge0.003Pd0.228Br0.164I2.836) with an optimal bandgap of 1.34 eV for single junction solar cells, 1511073 large-bandgap candidates (e.g., Cs0.392FA0.016MA0.592Cr0.383Sr0.347Sn0.270Br1.171I1.829) for top parts in tandem solar cells (TSCs), and 20242 low-bandgap ones (e.g., MA0.815FA0.185Sn0.927Ge0.073I3) for bottom cells in TSCs. Finally, three new HOIPs were synthesized with an average bandgap error 0.07 eV between predictions and experiments. We are convinced that the proposed PSP method and ML progress could facilitate the discovery of new promising HOIPs for photovoltaic devices with the desired properties.

Machine Learning-Assisted Design of Yttria-Stabilized Zirconia Thermal Barrier Coatings with High Bonding Strength.

As a high-quality thermal barrier coating material, yttria-stabilized zirconia (YSZ) can effectively reduce the temperature of the collective materials to be used on the surface of gas turbine hot-end components. The bonding strength between YSZ and the substrate is also one of the most important factors for the applications. Herein, the Gaussian mixture model (GMM) and support vector regression (SVR) were used to construct a machine learning model between YSZ coating bonding strength and atmospheric plasma spraying (APS) process parameters. First, GMM was used to expand the original 8 data points to 400 with the R value of leave-one-out cross-validation improved from 0.690 to 0.990. Then, the specific effects of APS process parameters were explored through Shapley additive explanations and sensitivity analysis. Principal component analysis was used to explain the constructed model and obtain the optimized area with a high bonding strength. After experimental validation, the results showed that under the APS process parameters of a current of 617 A, a voltage of 65 V, a H2 flow of 3 L min-1, and a thickness of 200 µm, the bonding strength increased by more than 19% to 55.5 MPa compared with the original maximum value of 46.6 MPa, indicating that the constructed GMM-SVR model can accurately predict the bonding strength of YSZ coating.

Liushen Capsules, a promising clinical candidate for COVID-19, alleviates SARS-CoV-2-induced pulmonary in vivo and inhibits the proliferation of the variant virus strains in vitro.

BACKGROUND: Coronavirus disease 2019 (COVID-19) causes a global pandemic and has devastating effects around the world, however, there are no specific antiviral drugs and vaccines for the constant mutation of SARS-CoV-2. PURPOSE: In this study, we evaluted the antiviral and anti-inflammatory activities of Liushen Capsules (LS) on different novel coronavirus in vitro, studied its therapeutic effects on novel SARS-CoV-2 infected mice and observed the LS's clinical efficacy and safety in COVID-19. METHODS: The antiviral and aiti-inflammatory effects of LS on the 501Y.V2/B.1.35 and G/478K.V1/ B.1.617.2 strains were determined in vitro. A hACE2 mouse model of novel SARS-CoV-2 pneumonia was established. Survival rates, histological changes, inflammatory markers, lung virus titers and the expression of the key proteins in the NF-κB/MAPK signaling pathway was detected by western blotting and immumohistochemical staining in the lungs were measured. Subsequently, the disease duration, prognosis of disease, time of negative nucleic acid and the cytokines levels in serum were used to assess the efficacy of treatment with LS in patients. RESULTS: The results showed that LS (2, 1, 0.5 µg/mL) could significantly inhibit the replication of the two SARS-CoV-2 variants and the expression of pro-inflammatory cytokines (IL-6, IL-8, IP-10, CCL-5, MIP-1α, IL-1α) induced by the virus in vitro. As for the survival experiment in mice, the survival rate of virus group was 20%, while LS-treatment groups (40, 80, 160 mg/kg) could increase the survival rate to 60, 100 and 100%, respectively. LS (40, 80, 160 mg/kg) could significantly decrease the lung titers in mice and it could improve the pathological changes, inhibit the excessive inflammatory mediators (IFN-α, IFN-γ, IP-10, MCP-1) and the protein expression of p-NF-κB p65 in mice. Moreover, LS could significantly decrease SARS-CoV-2-induced activation of p-NF-κB p65, p-IκBα, and p-p38 MAPK and increase the protein expression of the IκBα. In addition, the patient got complete relief of symptoms after being treated with LS for 6 days and was proven with negative PCR test after being treated for 23 days. Finally, treatment with LS could reduce the release of inflammatory cytokines (IL-6, PDGF-AA/BB, Eotaxin, MCP-1, MIP-1α, MIP-1ß, GRO, CCL-5, MCP-3, IP-10, IL-1α). CONCLUSION: LS effectively alleviated novel SARS-CoV-2 or variants induced pneumonia in vitro and in vivo, and improved the prognosis of COVID-19. In light of the efficacy and safety profiles, LS could be considered for the treatment of COVID-19 with a broad-spectrum antiviral and anti-inflammatory agent.

Predicting Experimental Formability of Hybrid Organic-Inorganic Perovskites via Imbalanced Learning.

Hybrid organic-inorganic perovskites (HOIPs) have gained lots of attention in the photovoltaic field, but their further development is restrained by contaminant and stability. More potential HOIPs should be explored for photovoltaic devices. In this work, we collected 539 HOIPs and 24 non-HOIPs experimentally synthesized to explore novel compositions of HOIPs. An imbalanced learning was carried out, and the best classification model achieved a leaving-one-out cross-validation accuracy of 100.0% and a test accuracy of 96.1%. The A site atomic radii (ARA), A site ionic radius (IRA), and tolerance factor (tf) were identified as the most important features. ARA < 2.72 Å, IRA < 2.65 Å, and tf < 1.01 contributed to perovskite formability, and the formability possibilities of the corresponding samples were over 90.0%. Potential A site organic fragments were identified for perovskite solar cells, such as dimethylamine, hydroxylamine, hydrazine, etc. Finally, three new Sn-Ge mixed systems of HOIPs were successfully synthesized, which was consistent with the model predictions.

Fe-N-C single-atom nanozymes based sensor array for dual signal selective determination of antioxidants.

Machine learning algorithms as a powerful tool can efficiently utilize and process large quantities of data generated by high-throughput experiments in various fields. In this work, we used a general ionic salt-assisted synthesis method to prepare oxidase-like Fe-N-C SANs. The possible reason for the excellent enzyme-mimicking activity and affinity of Fe-N-C SANs was further verified by density functional theory calculations. Due to the remarkable oxidase-mimicking activity, the prepared Fe-N-C SANs were used to detect ascorbic acid (AA) with a detection limit of 0.5 µM. Based on the machine learning algorithms, we successfully distinguished six antioxidants (ascorbic acid, glutathione, L-cysteine, dithiothreitol, uric acid, and dopamine) with the same concentration by either one kind of Fe-N-C SANs or three kinds of different Fe-N-C SANs. The usefulness of the Fe-N-C SANs sensor arrays was further validated by the hierarchal cluster analysis, where they also can be correctly identified. More importantly, a SANs-based digital-image colorimetric sensor array has also been successfully constructed and thereby achieved visual and informative colorimetric analysis for practical samples out of the lab. This work not only provides a design synthesis method to prepare SANs but also combines machine learning algorithms with SANs sensors to identify analytes with similar properties, which can further expand to the detection of proteins and cells related to diseases in the future.

Application of Machine Learning Methods for the Development of Antidiabetic Drugs.

Diabetes is a chronic non-communicable disease caused by several different routes, which has attracted increasing attention. In order to speed up the development of new selective drugs, machine learning (ML) technology has been applied in the process of diabetes drug development and opens up a new blueprint for drug design. This review provides a comprehensive portrayal of the application of ML in antidiabetic drug use.

Search for ABO3 Type Ferroelectric Perovskites with Targeted Multi-Properties by Machine Learning Strategies.

Ferroelectric perovskites are one of the most promising functional materials due to the pyroelectric and piezoelectric effect. In the practical applications of ferroelectric perovskites, it is often necessary to meet the requirements of multiple properties. In this work, a multiproperties machine learning strategy was proposed to accelerate the discovery and design of new ferroelectric ABO3-type perovskites. First, a classification model was constructed with data collected from publications to distinguish ferroelectric and nonferroelectric perovskites. The classification accuracies of LOOCV and the test set are 87.29% and 86.21%, respectively. Then, two machine learning strategies, Machine-Learning Workflow and SISSO, were used to construct the regression models to predict the specific surface area (SSA), band gap (Eg), Curie temperature (Tc), and dielectric loss (tan δ) of ABO3-type perovskites. The correlation coefficients of LOOCV in the optimal models for SSA, Eg, and Tc are 0.935, 0.891, and 0.971, respectively, while the correlation coefficient of the predicted and experimental values of the SISSO model for tan δ prediction could reach 0.913. On the basis of the models, 20 ABO3 ferroelectric perovskites with three different application prospects were screened out with the required properties, which could be explained by the patterns between the important descriptors and the properties by using SHAP. Furthermore, the constructed models were developed into web servers for the researchers to accelerate the rational design and discovery of ABO3 ferroelectric perovskites with desired multiple properties.

Physics-Based Feature Makes Machine Learning Cognizing Crystal Properties Simple.

Machine learning (ML) accelerates the rational design and discovery of materials, where the feature plays a critical role in the ML model training. We propose a low-cost electron probability waves (EPW) descriptor based on electronic structures, which is extracted from high-symmetry points in the Brillouin zone. In the task of distinguishing ferromagnetic or antiferromagnetic material, it achieves an accuracy (ACC) at 0.92 and an area under the receiver operating characteristic curve (AUC) at 0.83 by 10-fold cross-validation. Furthermore, EPW excels at classifying metal/semiconductors and judging the direct/indirect bandgap of semiconductors. The distribution of electron clouds is an essential criterion for the origin of ferromagnetism, and EPW acts as an emulation of the electronic structure, which is the key to the achievements. Our EPW-based ML model obtains ACC and AUC equivalent to crystal graph features-based deep learning models for tasks with physical recognitions in electronic states.

Predicting the Formability of Hybrid Organic-Inorganic Perovskites via an Interpretable Machine Learning Strategy.

Predicting the formability of perovskite structure for hybrid organic-inorganic perovskites (HOIPs) is a prominent challenge in the search for the required materials from a huge search space. Here, we propose an interpretable strategy combining machine learning with a shapley additive explanations (SHAP) approach to accelerate the discovery of potential HOIPs. According to the prediction of the best classification model, top-198 nontoxic candidates with a probability of formability (Pf) of >0.99 are screened from 18560 virtual samples. The SHAP analysis reveals that the radius and lattice constant of the B site (rB and LCB) are positively related to formability, while the ionic radius of the A site (rA), the tolerant factor (t), and the first ionization energy of the B site (I1B) have negative relations. The significant finding is that stricter ranges of t (0.84-1.12) and improved tolerant factor τ (critical value of 6.20) do exist for HOIPs, which are different from inorganic perovskites, providing a simple and fast assessment in the design of materials with an HOIP structure.

Machine Learning Aided Design of Polymer with Targeted Band Gap Based on DFT Computation.

Polymer band gap is one of the most important properties associated with electric conductivity. In this work, the machine learning model called support vector regression (SVR) was developed to predict the polymer band gap, where the training data of the polymer band gap were obtained from DFT computation while the descriptors were generated from Dragon. After feature selection with the maximum relevance minimum redundancy, the SVR model using 16 key features as inputs gave the optimal performance for predicting polymer band gaps. The determination coefficient (R2) of the SVR model between the DFT computations and SVR predictions of polymer band gaps reached as high as 0.824 for the leave-one-out cross-validation and 0.925 for the independent test. Besides, the 16 key features were explored through correlation analysis and sensitivity analysis. The available model can be used to screen out the polymers with targeted band gaps before experiments, which is very helpful for rapid design of new polymers.

Reducing Cd accumulation in rice grain with foliar application of glycerol and its mechanisms of Cd transport inhibition.

Excessive cadmium (Cd) in rice grain has become a major global public health problem. Here, the effect of foliar glycerol application on Cd accumulation in brown rice was examined. Various spraying concentrations of glycerol between 0.4mM and 50mM were investigated and the results showed that 0.8 mM was the best application concentration for decreasing Cd content in brown rice. After different application period experiment, filling stage was considered as the optimal spraying time. 0.4mM-5mM glycerol application one time at the filling stage could significantly reduce Cd concentration in brown rice by 28.5%-60.4%. Cd transport factors (the ratio of brown rice and flag leaf/node) were decreased by 48.5% and 27.3%, respectively, with glycerol application. Glycerol application also significantly increased Cd concentration in soluble fraction in flag leaf while reduced inorganic Cd and water-soluble Cd in both flag leaf and stem. Our results showed foliar spraying glycerol inhibited Cd transport to brown rice through Cd compartmentalisation in the vacuole and transformation of cadmium chemical form. This study may provide a new method to effectively alleviate the problem of excessive Cd in rice.

Preparation of a graft modified flocculant based on chitosan by ultrasonic initiation and its synergistic effect with kaolin for the improvement of acid blue 83 (AB 83) removal.

The chitosan (CTS) characterized with many functional amino and hydroxyl groups has been the research focus on flocculants preparation and synthesis because of the facile grafting and modification to enhance and enlarge its original functions. In this study, a new type of graft modified flocculant CTS-g-PAA and its combination with kaolin have been developed for the treatment of acid blue 83 (AB 83). The CTS-g-PAA prepared by ultrasonic initiation of acrylamide (AM), 3-Acrylamide propyltrimethylammonium chloride (AATPAC) and CTS. The factors affecting CTS-g-PAA molecular weight and CTS graft ratio were examined to have a better understanding and comprehending of the ultrasonic initiated polymerization. The structure and morphological characteristics of CTS-g-PAA were investigated and analyzed by infrared spectroscopy (FTIR), nuclear magnetic resonance spectroscopy (1H NMR), differential thermal/thermogravimetric (TG/DSC), scanning electron microscopy (SEM) and X-Ray Diffraction (XRD), respectively. The results indicated that the CTS cyclic structure was broken by ultrasonic initiation and the grafting occurred at amino group with C2 site of CTS. Meanwhile, the CTS-g-PAA coupled with kaolin exhibited superior flocculation performance better than the single removal effect, and the optimum removal rate for AB 83 could achieve 91.9%. The flocculation mechanism was discussed and summarized based on the conduction of zeta potential, FTIR of floc particles and the adsorption ability of the formed kaolin flocs. During the AB 83 flocculation, the CTS-g-PAA adsorption and flocculation contributed much to the AB 83 removal. The added kaolin particles played a crucial role in the enhancement of AB 83 adsorption and flocculation. The kaolin particles and the formed kaolin floc showed an adsorption effect for AB 83, and the kaolin particles also worked as the bridging between the CTS-g-PAA filled with AB 83 to strengthen the flocculation performance. As a result, the flocculation performance for AB 83 removal was greatly improved.

Mechanisms and Characteristics of Sulfonylureas and Glinides.

BACKGROUND: Type 2 diabetes mellitus is a complex progressive endocrine disease characterized by hyperglycemia and life-threatening complications. It is the most common disorder of pancreatic cell function that causes insulin deficiency. Sulfonylurea is a class of oral hypoglycemic drugs. Over the past half century, these drugs, together with the subsequent non-sulfonylureas (glinides), have been the main oral drugs for insulin secretion. OBJECTIVE: Through in-depth study, the medical profession considers it as an important drug for improving blood sugar control. METHODS: The mechanism, characteristics, efficacy and side effects of sulfonylureas and glinides were reviewed in detail. RESULTS: Sulfonylureas and glinides not only stimulated the release of insulin from pancreatic cells, but also had many extrapanular hypoglycemic effect, such as reducing the clearance rate of insulin in liver, reducing the secretion of glucagon, and enhancing the sensitivity of peripheral tissues to insulin in type 2 diabetes mellitus. CONCLUSION: Sulfonylureas and glinides are effective first-line drugs for the treatment of diabetes mellitus. Although they have the risk of hypoglycemia, weight gain and cardiovascular disease, their clinical practicability and safety can be guaranteed as long as they are reasonably used.

Machine-learning-assisted prediction of surgical outcomes in patients undergoing gastrectomy.

OBJECTIVE: Postoperative complications adversely affected the prognosis in patients with gastric cancer. This study intends to investigate the feasibility of using machine-learning model to predict surgical outcomes in patients undergoing gastrectomy. METHODS: In this study, cancer patients who underwent gastrectomy at Shanghai Rui Jin Hospital in 2017 were randomly assigned to a development or validation cohort in a 9:1 ratio. A support vector classification (SVC) model to predict surgical outcomes in patients undergoing gastrectomy was developed and further validated. RESULTS: A total of 321 patients with 32 features were collected. The positive and negative outcomes of postoperative complication after gastrectomy appeared in 100 (31.2%) and 221 (68.8%) patients, respectively. The SVC model was constructed to predict surgical outcomes in patients undergoing gastrectomy. The accuracy of 10-fold cross validation and external verification was 78.17% and 78.12%, respectively. Further, an online web server has been developed to share the SVC model for machine-learning-assisted prediction of surgical outcomes in patients undergoing gastrectomy in the future procedures, which is accessible at the web address: http://47.100.47.97:5005/r_model_prediction. CONCLUSIONS: The SVC model was a useful predictor for measuring the risk of postoperative complications after gastrectomy, which may help stratify patients with different overall status for choice of surgical procedure or other treatments. It can be expected that machine-learning models in cancer informatics research are possibly shareable and accessible via web address all over the world.

Inferring novel genes related to colorectal cancer via random walk with restart algorithm.

Colorectal cancer (CRC) is the third most common type of cancer. In recent decades, genomic analysis has played an increasingly important role in understanding the molecular mechanisms of CRC. However, its pathogenesis has not been fully uncovered. Identification of genes related to CRC as complete as possible is an important way to investigate its pathogenesis. Therefore, we proposed a new computational method for the identification of novel CRC-associated genes. The proposed method is based on existing proven CRC-associated genes, human protein-protein interaction networks, and random walk with restart algorithm. The utility of the method is indicated by comparing it to the methods based on Guilt-by-association or shortest path algorithm. Using the proposed method, we successfully identified 298 novel CRC-associated genes. Previous studies have validated the involvement of the majority of these 298 novel genes in CRC-associated biological processes, thus suggesting the efficacy and accuracy of our method.