Handbook on Big Data and Machine Learning in the Physical Sciences最新文献

Materials Informatics and Data System for Polymer Nanocomposites Analysis and Design 高分子纳米复合材料分析与设计的材料信息学与数据系统

Handbook on Big Data and Machine Learning in the Physical Sciences Pub Date : 2020-05-01 DOI: 10.1142/9789811204555_0003

Wei Chen, L. Schadler, C. Brinson, Yixing Wang, Yichi Zhang, A. Prasad, Xiaolin Li, Akshay Iyer

引用次数: 6

BACK MATTER 回到问题

Handbook on Big Data and Machine Learning in the Physical Sciences Pub Date : 2020-03-19 DOI: 10.1142/9789811204555_bmatter

S. Kalidindi, Sergei V. Kalinin, T. Lookman, I. Foster

引用次数: 0

Machine-Learning Assisted Structure Determination of Metallic Nanoparticles: A Benchmark 机器学习辅助金属纳米颗粒结构测定:一个基准

Handbook on Big Data and Machine Learning in the Physical Sciences Pub Date : 2020-03-19 DOI: 10.1142/9789811204579_0007

Yuewei Lin, M. Topsakal, J. Timoshenko, D. Lu, Shinjae Yoo, A. Frenkel

引用次数: 1

FRONT MATTER 前页

Handbook on Big Data and Machine Learning in the Physical Sciences Pub Date : 2020-03-19 DOI: 10.1142/9789811204555_fmatter

S. Kalidindi, Sergei V. Kalinin, T. Lookman, I. Foster

引用次数: 0

FRONT MATTER 前页

Handbook on Big Data and Machine Learning in the Physical Sciences Pub Date : 2020-03-19 DOI: 10.1142/9789811204579_fmatter

K. K. van Dam, K. Yager, Stuart Campbell, Richard Farnsworth, Maartje van Dam, Sergei V. Kalinin, I. Foster

引用次数: 0

Optimal Bounds on Nonlinear Partial Differential Equations in Model Certification, Validation, and Experiment Design 非线性偏微分方程在模型验证、验证和实验设计中的最优界

Handbook on Big Data and Machine Learning in the Physical Sciences Pub Date : 2020-03-19 DOI: 10.1142/9789811204579_0014 10.1142/11389

M. McKerns, F. Alexander, K. Hickmann, T. Sullivan, D. Sciences, Los Alamos National Laboratory, Computational Science Initiative, B. N. Laboratory, Vérification, Analysis, Institute of Applied Mathematics, Free University of Berlin

{"title":"Optimal Bounds on Nonlinear Partial Differential Equations in Model Certification, Validation, and Experiment Design","authors":"M. McKerns, F. Alexander, K. Hickmann, T. Sullivan, D. Sciences, Los Alamos National Laboratory, Computational Science Initiative, B. N. Laboratory, Vérification, Analysis, Institute of Applied Mathematics, Free University of Berlin","doi":"10.1142/9789811204579_0014 10.1142/11389","DOIUrl":"https://doi.org/10.1142/9789811204579_0014 10.1142/11389","url":null,"abstract":"We demonstrate that the recently developed Optimal Uncertainty Quantification (OUQ) theory, combined with recent software enabling fast global solutions of constrained non-convex optimization problems, provides a methodology for rigorous model certification, validation, and optimal design under uncertainty. In particular, we show the utility of the OUQ approach to understanding the behavior of a system that is governed by a partial differential equation -- Burgers' equation. We solve the problem of predicting shock location when we only know bounds on viscosity and on the initial conditions. Through this example, we demonstrate the potential to apply OUQ to complex physical systems, such as systems governed by coupled partial differential equations. We compare our results to those obtained using a standard Monte Carlo approach, and show that OUQ provides more accurate bounds at a lower computational cost. We discuss briefly about how to extend this approach to more complex systems, and how to integrate our approach into a more ambitious program of optimal experimental design.","PeriodicalId":318116,"journal":{"name":"Handbook on Big Data and Machine Learning in the Physical Sciences","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133011178","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: 2

BACK MATTER 回到问题

Handbook on Big Data and Machine Learning in the Physical Sciences Pub Date : 2020-03-19 DOI: 10.1142/9789811204579_bmatter

K. K. van Dam, K. Yager, Stuart Campbell, Richard Farnsworth, Maartje van Dam, Sergei V. Kalinin, I. Foster

引用次数: 0

Machine Learning Approaches for Extracting Process–Structure–Property Relationships from Experimental Data and Literature 从实验数据和文献中提取过程-结构-属性关系的机器学习方法

Handbook on Big Data and Machine Learning in the Physical Sciences Pub Date : 2020-03-19 DOI: 10.1142/9789811204555_0002

Nils Persson, Michael McBride, E. Reichmanis, M. Grover

引用次数: 0

Handbook on Big Data and Machine Learning in the Physical Sciences 物理科学中的大数据和机器学习手册

Handbook on Big Data and Machine Learning in the Physical Sciences Pub Date : 2020-03-02 DOI: 10.1142/11389

Sergei V. Kalinin, Ian T Foster, S. Kalidindi, T. Lookman, K. K. Dam, K. Yager, S. Campbell, R. Farnsworth, Maartje van Dam

引用次数: 12

High-Throughput Computational Studies in Catalysis and Materials Research, and Their Impact on Rational Design 催化和材料研究中的高通量计算研究及其对理性设计的影响

Handbook on Big Data and Machine Learning in the Physical Sciences Pub Date : 2019-02-11 DOI: 10.1142/9789811204555_0001

M. A. F. Afzal, J. Hachmann

{"title":"High-Throughput Computational Studies in Catalysis and Materials Research, and Their Impact on Rational Design","authors":"M. A. F. Afzal, J. Hachmann","doi":"10.1142/9789811204555_0001","DOIUrl":"https://doi.org/10.1142/9789811204555_0001","url":null,"abstract":"In the 21st century, many technology fields have become reliant on advancements in process automation. We have seen dramatic growth in areas and industries that have successfully implemented a high level of automation. In drug discovery, for example, it has alleviated an otherwise extremely complex and tedious process and has resulted in the development of several new drugs. Over the last decade, these automation techniques have begun being adapted in the chemical and materials community as well with the goal of exploring chemical space and pursuing the discovery and design of novel compounds for various applications. The impact of new materials on industrial and economic development has been stimulating tremendous research efforts by the materials community, and embracing automation as well as tools from computational and data science have led to acceleration and streamlining of the discovery process. In particular, virtual high-throughput screening (HTPS) is now becoming a mainstream technique to search for materials with properties that are tailored for specific applications. Its efficiency combined with the increasing availability of open-source codes and large computational resources makes it a powerful and attractive tool in materials research. Herein, we will review a selection of recent, high-profile HTPS projects for new materials and catalysts. In the case of catalysts, we focus on the HTPS studies for oxygen reduction reaction, oxygen evolution reaction, hydrogen evolution reaction, and carbon dioxide reduction reaction. Whereas, for other materials applications, we emphasize on the HTPS studies for photovoltaics, gas separation, high-refractive-index materials, and OLEDs.","PeriodicalId":318116,"journal":{"name":"Handbook on Big Data and Machine Learning in the Physical Sciences","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132574747","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