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您的检索式：作者名="Valentin Stanev"
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| 1 | Machine 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 | 2018 | npj Computational Materials2018，，1： | 21 |
| 2 | Identification of advanced spin-driven thermoelectric materials via interpretable machine learning显示文摘Machine learning is becoming a valuable tool for scientific discovery.Particularly attractive is the application of machine learning methods to the field of materials development,which enables innovations by discovering new and better functional materials.To apply machine learning to actual materials development,close collaboration between scientists and machine learning tools is necessary.However,such collaboration has been so far impeded by the black box nature of many machine learning algorithms.It is often difficult for scientists to interpret the data-driven models from the viewpoint of material science and physics.Here,we demonstrate the development of spin-driven thermoelectric materials with anomalous Nernst effect by using an interpretable machine learning method called factorized asymptotic Bayesian inference hierarchical mixture of experts(FAB/HMEs).Based on prior knowledge of material science and physics,we were able to extract from the interpretable machine learning some surprising correlations and new knowledge about spin-driven thermoelectric materials.Guided by this,we carried out an actual material synthesis that led to the identification of a novel spin-driven thermoelectric material.This material shows the largest thermopower to date. | Yuma Iwasaki Ryohto Sawada Valentin Stanev Masahiko Ishida Akihiro Kirihara Yasutomo Omori Hiroko Someya Ichiro Takeuchi Eiji Saitoh Shinichi Yorozu | 2019 | npj Computational Materials2019，，1： | 7 |
| 3 | Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering显示文摘Analyzing large X-ray diffraction(XRD)datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries.Optimizing and automating this task can help accelerate the process of discovery of materials with novel and desirable properties.Here,we report a new method for pattern analysis and phase extraction of XRD datasets.The method expands the Nonnegative Matrix Factorization method,which has been used previously to analyze such datasets,by combining it with custom clustering and cross-correlation algorithms.This new method is capable of robust determination of the number of basis patterns present in the data which,in turn,enables straightforward identification of any possible peak-shifted patterns.Peak-shifting arises due to continuous change in the lattice constants as a function of composition and is ubiquitous in XRD datasets from composition spread libraries.Successful identification of the peak-shifted patterns allows proper quantification and classification of the basis XRD patterns,which is necessary in order to decipher the contribution of each unique single-phase structure to the multi-phase regions.The process can be utilized to determine accurately the compositional phase diagram of a system under study.The presented method is applied to one synthetic and one experimental dataset and demonstrates robust accuracy and identification abilities. | Valentin Stanev Velimir V.Vesselinov A.Gilad Kusne Graham Antoszewski Ichiro Takeuchi Boian S.Alexandrov | 2018 | npj Computational Materials2018，，1： | 5 |
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