The Spatial Analysis of Lateral Bending on 40-m Prestressed Concrete T-Beam

Chen, Jian Ying; He, Guo Jing; Hua, Tang

doi:10.4028/www.scientific.net/amr.639-640.530

Cited by 6 publications

(6 citation statements)

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“…In this work, we tried more than one ML algorithm, such as K-nearest neighbor regression (KNR), random forest regression (RFR), support vector regression (SVR), gradient boosting regression (GBR), extreme gradient boosting regression (XGBR), and a kind of composited algorithms produced by TPOT (tree-based pipeline optimization tool) . For each algorithm, we tested various pairs of training and testing data with different separated ratios to gain ideal regression models.…”

Section: Computational Detailsmentioning

confidence: 99%

A Machine Learning Model on Simple Features for CO₂ Reduction Electrocatalysts

et al. 2020

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Electroreduction of CO2 is one of the most potential ways to realize CO2 recycle and energy regeneration. The key to promoting this technology is the development of high-performance electrocatalysts. Generally, high-throughput computational screening contributes a lot to materials innovation, but still consumes much time and resource. To achieve efficient exploration of electrocatalysts for CO2 reduction, we created a machine learning model based on an extreme gradient boosting regression (XGBR) algorithm and simple features. Our screening model successfully and rapidly predicted the Gibbs free energy change of CO adsorption (ΔG CO) of 1060 atomically dispersed metal–nonmetal codoped graphene systems, and greatly reduced the research cost. The competitive reaction, the hydrogen evolution reaction (HER), is also discussed with respect to such a screening model. This work demonstrates the potential of machine learning methods and provides a convenient approach for the effective theoretical design of electrocatalysts for CO2 reduction.

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Section: Computational Detailsmentioning

confidence: 99%

A Machine Learning Model on Simple Features for CO₂ Reduction Electrocatalysts

et al. 2020

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“…Supervised classification ML was trained and established by extreme gradient boosting (XGB) algorithms and LR from the scikit-learn package . The Pearson correlation coefficient ( p ) and feature importance ( F i ) were applied to determine the performance of training features.…”

Section: Computational Methodsmentioning

confidence: 99%

Accelerated Mining of 2D Van der Waals Heterojunctions by Integrating Supervised and Unsupervised Learning

et al. 2022

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The contact resistance of the metal−semiconductor interface at the source and drain increases as the injection of carriers decreases, degrading the device performance. Reducing the Schottky barrier height (SBH) at the metal−semiconductor interface is an effective way of reducing the contact resistance. Therefore, exploring Ohmic contact (no Schottky barrier) or low contact resistance two-dimensional (2D) semiconductor/metal heterojunctions is critical for developing high-performance electronic devices. We generate a comprehensive dataset from the periodic table consisting of 1092 potential 2D semiconductor/metal heterojunctions with good contact performances and demonstrate that the small interfacial dipole and the elimination of localized surface states are essential for designing advanced 2D metal−semiconductor systems with small SBHs. We use integrated supervised and unsupervised learning, as well as first-principles calculations, to screen 6 potential 2D van der Waals metal− semiconductor heterojunctions (BTe−NbSe 2 , Al 2 SO−Zn 3 C 2 , iAl 2 SO−Zn 3 C 2 , GaSe− NbS 2 , GaSe−NbSe 2 , and GeSe−VS 2 ) with Ohmic contact and high tunneling probabilities from 1092 candidates. The proposed method takes less than 5 s to execute and is far superior to traditional first-principles calculations in both time and cost, demonstrating the superiority of using machine learning for screening materials and that unsupervised assisted algorithm can alleviate the problem of data scarcity to predict the behaviors of complex dynamical systems.

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“…For the Fisher’s exact test, features were selected at five different p -value cutoffs ranging from 0.01 to 0.05 with an interval of 0.01. For the AUC-ROC, features were selected at five different cutoffs ranging from 0.52 to 0.60 with an interval 0.02 using the “ROCR” and “pROC” packages. , The “xgboost” and “Random Forest” packages were applied to retrieve feature importance scores, respectively. Features ranked with Gain or Gini scores were picked at 10 intervals from the top 10 to top 50.…”

Section: Methodsmentioning

confidence: 99%

Systematic Identification of Molecular Targets and Pathways Related to Human Organ Level Toxicity

Xia

et al. 2020

Chem. Res. Toxicol.

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The mechanisms leading to organ level toxicities are poorly understood. In this study, we applied an integrated approach to deduce the molecular targets and biological pathways involved in chemically induced toxicity for eight common human organ level toxicity end points (carcinogenicity, cardiotoxicity, developmental toxicity, hepatotoxicity, nephrotoxicity, neurotoxicity, reproductive toxicity, and skin toxicity). Integrated analysis of in vitro assay data, molecular targets and pathway annotations from the literature, and toxicity–molecular target associations derived from text mining, combined with machine learning techniques, were used to generate molecular targets for each of the organ level toxicity end points. A total of 1516 toxicity-related genes were identified and subsequently analyzed for biological pathway coverage, resulting in 206 significant pathways (p-value <0.05), ranging from 3 (e.g., developmental toxicity) to 101 (e.g., skin toxicity) for each toxicity end point. This study presents a systematic and comprehensive analysis of molecular targets and pathways related to various in vivo toxicity end points. These molecular targets and pathways could aid in understanding the biological mechanisms of toxicity and serve as a guide for the design of suitable in vitro assays for more efficient toxicity testing. In addition, these results are complementary to the existing adverse outcome pathway (AOP) framework and can be used to aid in the development of novel AOPs. Our results provide abundant testable hypotheses for further experimental validation.

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The Spatial Analysis of Lateral Bending on 40-m Prestressed Concrete T-Beam

Cited by 6 publications

References 0 publications

A Machine Learning Model on Simple Features for CO₂ Reduction Electrocatalysts

A Machine Learning Model on Simple Features for CO₂ Reduction Electrocatalysts

Accelerated Mining of 2D Van der Waals Heterojunctions by Integrating Supervised and Unsupervised Learning

Systematic Identification of Molecular Targets and Pathways Related to Human Organ Level Toxicity

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The Spatial Analysis of Lateral Bending on 40-m Prestressed Concrete T-Beam

Cited by 6 publications

References 0 publications

A Machine Learning Model on Simple Features for CO2 Reduction Electrocatalysts

A Machine Learning Model on Simple Features for CO2 Reduction Electrocatalysts

Accelerated Mining of 2D Van der Waals Heterojunctions by Integrating Supervised and Unsupervised Learning

Systematic Identification of Molecular Targets and Pathways Related to Human Organ Level Toxicity

Contact Info

Product

Resources

About

A Machine Learning Model on Simple Features for CO₂ Reduction Electrocatalysts

A Machine Learning Model on Simple Features for CO₂ Reduction Electrocatalysts