生成和分析热物理数据的机器学习:我们的现状与未来

IF 2 3区 工程技术 Q3 CHEMISTRY, MULTIDISCIPLINARY Journal of Chemical & Engineering Data Pub Date : 2024-06-13 DOI:10.1021/acs.jced.4c00207
Rose K. Cersonsky*, Bingqing Cheng, David Kofke and Erich A. Müller, 
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Machine Learning for Generating and Analyzing Thermophysical Data: Where We Are and Where We’re Going
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来源期刊
Journal of Chemical & Engineering Data
Journal of Chemical & Engineering Data 工程技术-工程:化工
CiteScore
5.20
自引率
19.20%
发文量
324
审稿时长
2.2 months
期刊介绍: The Journal of Chemical & Engineering Data is a monthly journal devoted to the publication of data obtained from both experiment and computation, which are viewed as complementary. It is the only American Chemical Society journal primarily concerned with articles containing data on the phase behavior and the physical, thermodynamic, and transport properties of well-defined materials, including complex mixtures of known compositions. While environmental and biological samples are of interest, their compositions must be known and reproducible. As a result, adsorption on natural product materials does not generally fit within the scope of Journal of Chemical & Engineering Data.
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