Science and Technology of Advanced Materials: Methods最新文献_第5页

Data integration for multiple alkali metals in predicting coordination energies based on Bayesian inference 基于贝叶斯推理的多碱金属配位能预测数据集成

Science and Technology of Advanced Materials: Methods Pub Date : 2022-09-26 DOI: 10.1080/27660400.2022.2108353

Koki Obinata, Tomofumi Nakayama, Atsushi Ishikawa, K. Sodeyama, K. Nagata, Y. Igarashi, M. Okada

{"title":"Data integration for multiple alkali metals in predicting coordination energies based on Bayesian inference","authors":"Koki Obinata, Tomofumi Nakayama, Atsushi Ishikawa, K. Sodeyama, K. Nagata, Y. Igarashi, M. Okada","doi":"10.1080/27660400.2022.2108353","DOIUrl":"https://doi.org/10.1080/27660400.2022.2108353","url":null,"abstract":"ABSTRACT Building machine learning models using a dataset calculated by first principles calculations is an important approach to explore the next-generation batteries. In previous studies, the rechargeable secondary battery dataset was constructed for Li ion by density functional theory (DFT) calculation, and after that, extended to five alkali metal ions. This dataset can be regarded as consisting of five alkali metal ion groups, and it is one of the interests to know which approach is preferred to build individual models specialized for each alkali metal ion or build a single model by integrating the datasets. We quantitatively evaluate the possibility of data integration in the framework of Bayesian model selection and show that the integration of datasets is suitable. In addition, extracting new knowledge using feature selection is also important in exploring next-generation batteries. In order to further advance the knowledge extraction, the reliability of the selected features should be considered to avoid misinterpretation. We evaluate the confidence level of feature selection by calculating the posterior probabilities of features using Bayesian model averaging (BMA). We found that the confidence level of feature selection increases when the number of data increases. GRAPHICAL ABSTRACT","PeriodicalId":221393,"journal":{"name":"Science and Technology of Advanced Materials: Methods","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132464482","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

Efficient autonomous material search method combining ab initio calculations, autoencoder, and multi-objective Bayesian optimization 结合从头算、自动编码器和多目标贝叶斯优化的高效自主材料搜索方法

Science and Technology of Advanced Materials: Methods Pub Date : 2022-09-23 DOI: 10.1080/27660400.2022.2123263

Yuma Iwasaki, Hwang Jaekyun, Y. Sakuraba, M. Kotsugi, Y. Igarashi

引用次数: 3

Prediction model of elastic constants of BCC high-entropy alloys based on first-principles calculations and machine learning techniques 基于第一性原理计算和机器学习技术的BCC高熵合金弹性常数预测模型

Science and Technology of Advanced Materials: Methods Pub Date : 2022-09-22 DOI: 10.1080/27660400.2022.2125853

G. Hayashi, K. Suzuki, T. Terai, H. Fujii, M. Ogura, K. Sato

{"title":"Prediction model of elastic constants of BCC high-entropy alloys based on first-principles calculations and machine learning techniques","authors":"G. Hayashi, K. Suzuki, T. Terai, H. Fujii, M. Ogura, K. Sato","doi":"10.1080/27660400.2022.2125853","DOIUrl":"https://doi.org/10.1080/27660400.2022.2125853","url":null,"abstract":"ABSTRACT By combining first-principles electronic structure calculations and machine learning techniques, prediction models of elastic constants are constructed for BCC high-entropy alloys (HEA) containing 5 different elements chosen from 3d, 4d and 5d transition metals with equal concentration. Three independent elastic constants of randomly selected 2555 HEAs are calculated by using the full potential Korringa–Kohn–Rostoker (FPKKR) method with taking configurational disorder into account within the coherent potential approximation (CPA). From the obtained database of the elastic constants, prediction models are constructed by the linear regression using the descriptors generated by the linearly independent descriptor generation (LIDG) method. By optimizing the selection of descriptors based on the genetic algorithm (GA), prediction errors of 10.2 GPa, 4.5 GPa, 2.4 GPa and 7.7 GPa are achieved for bulk modulus , shear moduli , and Young’s modulus , respectively. By using the generated model we propose some HEAs with low . It is well known that the magnitude of is closely related to the shape of the calculated density of states (DOS). This statement is reconfirmed within the BCC HEAs, i.e., HEAs with larger DOS at the Fermi level shows smaller Young’s modulus and vice versa. Graphical abstract","PeriodicalId":221393,"journal":{"name":"Science and Technology of Advanced Materials: Methods","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126945256","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: 3

Prediction of continuous cooling transformation diagram for weld heat affected zone by machine learning 基于机器学习的焊接热影响区的连续冷却转变图预测

Science and Technology of Advanced Materials: Methods Pub Date : 2022-09-21 DOI: 10.1080/27660400.2022.2123262

S. Minamoto, S. Tsukamoto, T. Kasuya, M. Watanabe, M. Demura

引用次数: 2

Modeling interfaces of fluorite-structure compounds using slab charge distribution 用平板电荷分布模拟萤石结构化合物的界面

Science and Technology of Advanced Materials: Methods Pub Date : 2022-09-21 DOI: 10.1080/27660400.2022.2126739

Y. Hinuma

引用次数: 0

MaterialBERT for natural language processing of materials science texts 材料科学文本的自然语言处理

Science and Technology of Advanced Materials: Methods Pub Date : 2022-09-16 DOI: 10.1080/27660400.2022.2124831

M. Yoshitake, Fumitaka Sato, Hiroyuki Kawano, H. Teraoka

引用次数: 3

Machine learning prediction on intermetallic compounds with implemented virtual-center-atom structural descriptor 基于虚拟中心-原子结构描述符的金属间化合物机器学习预测

Science and Technology of Advanced Materials: Methods Pub Date : 2022-09-01 DOI: 10.1080/27660400.2022.2119111

Zhenwei Li, Junjie Wang

引用次数: 0

Prediction of grain boundary chemistry in multicomponent alloys 多组分合金的晶界化学预测

Science and Technology of Advanced Materials: Methods Pub Date : 2022-08-15 DOI: 10.1080/27660400.2022.2112915

Masataka Funamoto, Yusuke Matsuoka, Yuhki Tsukada, T. Koyama

{"title":"Prediction of grain boundary chemistry in multicomponent alloys","authors":"Masataka Funamoto, Yusuke Matsuoka, Yuhki Tsukada, T. Koyama","doi":"10.1080/27660400.2022.2112915","DOIUrl":"https://doi.org/10.1080/27660400.2022.2112915","url":null,"abstract":"ABSTRACT Hillert’s grain-boundary-phase (GBP) model is employed for predicting grain boundary (GB) chemistry in multicomponent alloys. The GB is approximated as a thin layer of a homogeneous phase with a constant thickness and its own Gibbs energy. The GB composition is computed to minimize the Gibbs energy of the mixture of a grain phase and the GBP; the Gibbs energy of liquid phase is assigned to that of the GBP. The calculation of phase diagram (CALPHAD) databases are employed to calculate the Gibbs energy of a phase of interest as a function of composition and temperature. To verify the calculation results’ validity, the predicted GB chemistry was compared with experimental data from previous research for nickel-based superalloys, an austenitic stainless steel, and a high-entropy alloy. It is demonstrated that the method combining Hillert’s GBP model and CALPHAD databases is effective for predicting the equilibrium solute segregation to stationary random high-angle GBs in multicomponent alloys, enabling the advanced compositional design of materials for GB segregation engineering. GRAPHICAL ABSTRACT","PeriodicalId":221393,"journal":{"name":"Science and Technology of Advanced Materials: Methods","volume":"15 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133616776","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: 3

Effects of data bias on machine-learning–based material discovery using experimental property data 数据偏差对基于机器学习的材料发现使用实验属性数据的影响

Science and Technology of Advanced Materials: Methods Pub Date : 2022-08-04 DOI: 10.1080/27660400.2022.2109447

Masaya Kumagai, Yuki Ando, Atsumi Tanaka, Koji Tsuda, Y. Katsura, Ken Kurosaki

{"title":"Effects of data bias on machine-learning–based material discovery using experimental property data","authors":"Masaya Kumagai, Yuki Ando, Atsumi Tanaka, Koji Tsuda, Y. Katsura, Ken Kurosaki","doi":"10.1080/27660400.2022.2109447","DOIUrl":"https://doi.org/10.1080/27660400.2022.2109447","url":null,"abstract":"ABSTRACT Materials informatics (MI) research, which is the discovery of new materials through machine learning (ML) using large-scale material data, has attracted considerable attention in recent years. However, in general, the large-scale material data used in MI are biased owing to differences in the targeted material domains. Moreover, most studies on MI have not clearly demonstrated the influence of data bias on ML models. In this study, we clarify the influence of data bias on ML models by combining the concept of the applicability domain and clustering for large-scale experimental property data in the Starrydata2 material database previously developed by our group. The results show that data bias influences the error and reliability of the predictions made by the ML model. The predictions of the ML model within the applicability domain are highly reliable compared to those made outside the domain. This indicates that the material space that can be reliably discovered by the constructed ML model is limited. Nonetheless, we apply the ML model to a large dataset comprising various material classes and find that new materials similar to known materials can be proposed within a limited space. Thus, our findings demonstrate the importance of considering data bias when constructing and evaluating ML models in MI. GRAPHICAL ABSTRACT","PeriodicalId":221393,"journal":{"name":"Science and Technology of Advanced Materials: Methods","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123441549","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: 3

Prediction of operating dynamics in floating-zone crystal growth using Gaussian mixture model 用高斯混合模型预测浮区晶体生长的运行动力学

Science and Technology of Advanced Materials: Methods Pub Date : 2022-08-03 DOI: 10.1080/27660400.2022.2107884

R. Omae, S. Sumitani, Y. Tosa, S. Harada

引用次数: 1