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4篇 您的检索式:作者名="Efrain Rodriguez"
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1Machine learning modeling of superconducting critical temperature显示文摘Superconductivity has been the focus of enormous research effort since its discovery more than a century ago.Yet,some features of this unique phenomenon remain poorly understood;prime among these is the connection between superconductivity and chemical/structural properties of materials.To bridge the gap,several machine learning schemes are developed herein to model the critical temperatures(T_(c))of the 12,000+known superconductors available via the SuperCon database.Materials are first divided into two classes based on their T_(c) values,above and below 10 K,and a classification model predicting this label is trained.The model uses coarse-grained features based only on the chemical compositions.It shows strong predictive power,with out-of-sample accuracy of about 92%.Separate regression models are developed to predict the values of T_(c) for cuprate,iron-based,and low-T_(c) compounds.These models also demonstrate good performance,with learned predictors offering potential insights into the mechanisms behind superconductivity in different families of materials.To improve the accuracy and interpretability of these models,new features are incorporated using materials data from the AFLOW Online Repositories.Finally,the classification and regression models are combined into a single-integrated pipeline and employed to search the entire Inorganic Crystallographic Structure Database(ICSD)for potential new superconductors.We identify>30 non-cuprate and non-iron-based oxides as candidate materials.Valentin Stanev Corey Oses A.Gilad Kusne Efrain Rodriguez Johnpierre Paglione Stefano Curtarolo Ichiro Takeuchi 2018npj Computational Materials2018,,1:21
2New frontiers for the materials genome initiative显示文摘The Materials Genome Initiative(MGI)advanced a new paradigm for materials discovery and design,namely that the pace of new materials deployment could be accelerated through complementary efforts in theory,computation,and experiment.Along with numerous successes,new challenges are inviting researchers to refocus the efforts and approaches that were originally inspired by the MGI.In May 2017,the National Science Foundation sponsored the workshop“Advancing and Accelerating Materials Innovation Through the Synergistic Interaction among Computation,Experiment,and Theory:Opening New Frontiers”to review accomplishments that emerged from investments in science and infrastructure under the MGI,identify scientific opportunities in this new environment,examine how to effectively utilize new materials innovation infrastructure,and discuss challenges in achieving accelerated materials research through the seamless integration of experiment,computation,and theory.This article summarizes key findings from the workshop and provides perspectives that aim to guide the direction of future materials research and its translation into societal impacts.Juan J.de Pablo Nicholas E.Jackson Michael A.Webb Long-Qing Chen Joel E.Moore Dane Morgan Ryan Jacobs Tresa Pollock Darrell G.Schlom Eric S.Toberer James Analytis Ismaila Dabo Dean M.DeLongchamp Gregory A.Fiete Gregory M.Grason Geoffroy Hautier Yifei Mo Krishna Rajan Evan J.Reed Efrain Rodriguez Vladan Stevanovic Jin Suntivich Katsuyo Thornton Ji-Cheng Zhao 2019npj Computational Materials2019,,1:7
3Encapsulation of Glycosides from Extract Plantago Major in ZnO Matrices by Sol-Gel显示文摘Javier Aguila Rosas Albino Moreno Rodriguez Alfonso D. Diaz Fonseca Alan E. Aguilar Aguilar Carmen M. Atzin Macedo Veronica Santander Vazquez Claudia Santander Vazquez Efrain Rubio Rosas Lilian A. Moreno Rodriguez 2015材料科学与工程(中英文A版)2015,5,3:0
4Synthesis and Characterization of TiO2 Nanoreservoirs Supported with Platinum II and Sodium Bicarbonate Solution显示文摘Jose Albino Moreno Rodriguez Genaro Carmona Gutierrez Marco-Antonio Gonzalez Coronell Efrain Rubio Rosas Lilian-Aurora Moreno Rodrlguez Fernando Moreno Rodriguez 2015材料科学与工程(中英文A版)2015,5,1:0
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