检索结果-南通市图书馆

Analyzing machine learning models to accelerate generation of fundamental materials insights

npj Computational Materials 2019年第1期5卷 858-866页

作者： mitsutaro umehara Helge S.Stein Dan Guevarra Paul F.Newhouse David A.Boyd John M.Gregoire Joint Center for Artificial Photosynthesis California Institute of TechnologyPasadenaCA 91125USA Future Mobility Research Department Toyota Research Institute of North AmericaAnn ArborMI 48105USA

Machine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding.A primary role of scientists is extraction of fundamental knowledge from data,and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction *** neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces,such as that mapped by a combinatorial materials science *** a performance metric in a given materials space provides direct information about(locally)optimal materials but not the underlying materials science that gives rise to the variation in *** building a model that predicts performance(in this case photoelectrochemical power generation of a solar fuels photoanode)from materials parameters(in this case composition and Raman signal),subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical *** interpretation of these key relationships produces the desired fundamental understanding,demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human *** also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space,such as the addition of specific alloying elements,that may increase performance by moving beyond the confines of existing data.

关键词： neural prediction desired

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Multi-component background learning automates signal detection for spectroscopic data

引用

npj Computational Materials 2019年第1期5卷 484-490页

作者： Sebastian E.Ament Helge S.Stein Dan Guevarra Lan Zhou Joel A.Haber David A.Boyd mitsutaro umehara John M.Gregoire Carla P.Gomes Department of Computer Science Cornell UniversityIthacaNY 14850USA Joint Center for Artificial Photosynthesis California Institute of TechnologyPasadenaCA 91125USA Future Mobility Research Department Toyota Research Institute of North AmericaAnn ArborMI 48105USA

Automated experimentation has yielded data acquisition rates that supersede human processing *** Intelligence offers new possibilities for automating data interpretation to generate large,high-quality *** subtraction is a long-standing challenge,particularly in settings where multiple sources of the background signal coexist,and automatic extraction of signals of interest from measured signals accelerates data ***,we present an unsupervised probabilistic learning approach that analyzes large data collections to identify multiple background sources and establish the probability that any given data point contains a signal of *** approach is demonstrated on X-ray diffraction and Raman spectroscopy data and is suitable to any type of data where the signal of interest is a positive addition to the background *** the model can incorporate prior knowledge,it does not require knowledge of the signals since the shapes of the background signals,the noise levels,and the signal of interest are simultaneously learned via a probabilistic matrix factorization *** identification of interpretable signals by unsupervised probabilistic learning avoids the injection of human bias and expedites signal extraction in large datasets,a transformative capability with many applications in the physical sciences and beyond.

关键词： signal component spectroscopic

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：