Science and Technology of Advanced Materials: Methods最新文献_第3页

DMC-TPE: Tree-structured Parzen estimator-based efficient data assimilation method for phase-field simulation of solid-state sintering 基于树结构Parzen估计的固体烧结相场模拟数据同化方法

Science and Technology of Advanced Materials: Methods Pub Date : 2023-07-20 DOI: 10.1080/27660400.2023.2239133

Akimitsu Ishii, Akiyasu Yamamoto, A. Yamanaka

引用次数: 0

NIMS fatigue data sheet on low- and high-cycle fatigue properties of A2017-T4 (Al-4.0Cu-0.6Mg) aluminium alloy A2017-T4 (Al-4.0Cu-0.6Mg)铝合金低周和高周疲劳性能的NIMS疲劳数据表

Science and Technology of Advanced Materials: Methods Pub Date : 2023-07-10 DOI: 10.1080/27660400.2023.2234272

Y. Furuya, Hideaki Nishikawa, H. Hirukawa, N. Nagashima, Etsuo Takeuchi

引用次数: 1

Cylindrically symmetric rotating crystals observed in crystallization process of InSiO film 在inso薄膜的结晶过程中观察到圆柱对称的旋转晶体

Science and Technology of Advanced Materials: Methods Pub Date : 2023-06-29 DOI: 10.1080/27660400.2023.2230870

Bo Da, Long Cheng, Xun Liu, K. Shigeto, K. Tsukagoshi, T. Nabatame, Zejun Ding, Yang Sun, Jin Hu, Jiangwei Liu, D. Tang, Han Zhang, Z. Gao, Hongxuan Guo, Hideki Yoshikawa, S. Tanuma

引用次数: 0

Structuring superconductor data with ontology: reproducing historical datasets as knowledge bases 用本体构造超导体数据:再现历史数据集作为知识库

Science and Technology of Advanced Materials: Methods Pub Date : 2023-06-21 DOI: 10.1080/27660400.2023.2223051

Masashi Ishii, Koichi Sakamoto

引用次数: 1

Experimental exploration of ErB2 and SHAP analysis on a machine-learned model of magnetocaloric materials for materials design 用于材料设计的磁热材料机器学习模型的ErB2和SHAP分析的实验探索

Science and Technology of Advanced Materials: Methods Pub Date : 2023-06-21 DOI: 10.1080/27660400.2023.2217474

K. Terashima, Pedro Baptista de Castro, A. Saito, Takafumi D. Yamamoto, R. Matsumoto, H. Takeya, Y. Takano

{"title":"Experimental exploration of ErB2 and SHAP analysis on a machine-learned model of magnetocaloric materials for materials design","authors":"K. Terashima, Pedro Baptista de Castro, A. Saito, Takafumi D. Yamamoto, R. Matsumoto, H. Takeya, Y. Takano","doi":"10.1080/27660400.2023.2217474","DOIUrl":"https://doi.org/10.1080/27660400.2023.2217474","url":null,"abstract":"Stimulated by a recent report of a giant magnetocaloric effect in HoB$_2$ found via machine-learning predictions, we have explored the magnetocaloric properties of a related compound ErB$_2$, that has remained the last ferromagnetic material among the rare-earth diboride (REB$_2$) family with unreported magnetic entropy change |{Delta}SM|. The evaluated $|Delta S_M|$ at field change of 5 T in ErB$_2$ turned out to be as high as 26.1 (J kg$^{-1}$ K$^{-1}$) around the ferromagnetic transition (${T_C}$) of 14 K. In this series, HoB$_2$ is found to be the material with the largest $|Delta S_M|$ as the model predicted, while the predicted values showed a deviation with a systematic error compared to the experimental values. Through a coalition analysis using SHAP, we explore how this rare-earth dependence and the deviation in the prediction are deduced in the model. We further discuss how SHAP analysis can be useful in clarifying favorable combinations of constituent atoms through the machine-learned model with compositional descriptors. This analysis helps us to perform materials design with aid of machine learning of materials data.","PeriodicalId":221393,"journal":{"name":"Science and Technology of Advanced Materials: Methods","volume":"121 20","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120842322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Prediction of Born effective charges using neural network to study ion migration under electric fields: applications to crystalline and amorphous Li3PO4 利用神经网络预测电场下离子迁移的Born有效电荷:在晶体和非晶Li3PO4中的应用

Science and Technology of Advanced Materials: Methods Pub Date : 2023-05-31 DOI: 10.1080/27660400.2023.2253135

Koji Shimizu, Ryuji Otsuka, M. Hara, E. Minamitani, S. Watanabe

{"title":"Prediction of Born effective charges using neural network to study ion migration under electric fields: applications to crystalline and amorphous Li3PO4","authors":"Koji Shimizu, Ryuji Otsuka, M. Hara, E. Minamitani, S. Watanabe","doi":"10.1080/27660400.2023.2253135","DOIUrl":"https://doi.org/10.1080/27660400.2023.2253135","url":null,"abstract":"Understanding ionic behaviour under external electric fields is crucial to develop electronic and energy-related devices using ion transport. In this study, we propose a neural network (NN) model to predict the Born effective charges of ions along an axis parallel to an applied electric field from atomic structures. The proposed NN model is applied to Li$_3$PO$_4$ as a prototype. The prediction error of the constructed NN model is 0.0376 $e$/atom. In combination with an NN interatomic potential, molecular dynamics (MD) simulations are performed under a uniform electric field of 0.1 V/angstrom, whereby an enhanced mean square displacement of Li along the electric field is obtained, which seems physically reasonable. In addition, the external forces along the direction perpendicular to the electric field, originating from the off-diagonal terms of the Born effective charges, are found to have a nonnegligible effect on Li migration. Finally, additional MD simulations are performed to examine the Li motion in an amorphous structure. The results reveal that Li migration occurs in various areas despite the absence of explicitly introduced defects, which may be attributed to the susceptibility of the Li ions in the local minima to the electric field. We expect that the proposed NN method can be applied to any ionic material, thereby leading to atomic-scale elucidation of ion behaviour under electric fields.","PeriodicalId":221393,"journal":{"name":"Science and Technology of Advanced Materials: Methods","volume":"16 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132359023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tuning Bayesian optimization for materials synthesis: simulating two- and three-dimensional cases 材料合成的贝叶斯优化:模拟二维和三维情况

Science and Technology of Advanced Materials: Methods Pub Date : 2023-05-04 DOI: 10.1080/27660400.2023.2210251

Han Xu, Ryozo Nakayama, Takefumi Kimura, R. Shimizu, Y. Ando, Shigeru Kobayashi, Nobuaki Yasuo, M. Sekijima, T. Hitosugi

引用次数: 0

Automatic knowledge acquisition from superconductivity information in literature 从文献中的超导信息中自动获取知识

Science and Technology of Advanced Materials: Methods Pub Date : 2023-05-02 DOI: 10.1080/27660400.2023.2206532

Kento Mitsui, Yutaka Sasaki, Ryoji Asahi

引用次数: 0

A Bayesian Method for Concurrently Designing Molecules and Synthetic Reaction Networks 同时设计分子和合成反应网络的贝叶斯方法

Science and Technology of Advanced Materials: Methods Pub Date : 2023-04-19 DOI: 10.1080/27660400.2023.2204994

Qi Zhang, Chang Liu, Stephen Wu, Y. Hayashi, Ryo Yoshida

引用次数: 1

Visualizing local bending of lattice planes by extending two-azimuth synchrotron X-ray diffraction datasets to asymmetric reflection 将双方位角同步加速器x射线衍射数据集扩展到不对称反射的晶格面局部弯曲可视化

Science and Technology of Advanced Materials: Methods Pub Date : 2023-04-11 DOI: 10.1080/27660400.2023.2199130

O. Sakata, S. Yagyu

引用次数: 0