Sampling Methodologies for High Throughput Materials Discovery

被引:0
作者
Baumes, Laurent A. [1 ]
Vicente de Julian-Ortiz, Jesus [2 ]
机构
[1] UPV CSIC, Inst Tecnol Quim, Ave Los Naranjos 0, E-46022 Valencia, Spain
[2] Univ Valencia, Fac Farm, Dept Quim Fis, Estudi Gen, Ave V Andres Estelles 0, E-46100 Valencia, Spain
关键词
ARTIFICIAL NEURAL-NETWORKS; HOLOGRAPHIC RESEARCH STRATEGY; GENETIC ALGORITHM; EXPERIMENTAL INCONSISTENCY; OXIDATION CATALYSIS; EXPERIMENTAL-DESIGN; COMBINATORIAL; OPTIMIZATION; EXPERIMENTATION; LIBRARY;
D O I
暂无
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
A mini-review of developments in sampling algorithms applied to the design of new catalysts and materials is presented. The. data mining technology is increasingly being used in new industrial processes, which require automatic analysis of data and related results to proceed quickly to conclusions. However, for some applications, absolute automation may not be. appropriate. Unlike traditional data mining contexts, processing of large amounts of data, some domains are characterized by the scarcity of data, due to the cost and time involved in the realization of simulations or the setting up of experimental apparatuses for the collection of data. In such domains, therefore, it is prudent to balance the speed through the automation and the utility of the generated data.
引用
收藏
页码:511 / 526
页数:16
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