Machine learning enabled high-throughput screening of hydrocarbon molecules for the design of next generation fuels

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Machine learning enabled high-throughput screening of hydrocarbon molecules for the design of next generation fuels

机译：机器学习可对烃分子进行高通量筛选，以设计下一代燃料

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Next generation high energy density hydrocarbon (HEDH) fuels are urgently demanded to extend the range of propulsion system and meet additional requirements of new engines. We develop a facile and efficient methodology based on machine learning enabled high-throughput screening to accelerate the design of next generation fuels, and present a proof-of-concept study for discovering new HEDH fuels. This approach screens 319,895 hydrocarbon molecules using the key properties of fuel as the threshold values, and a group of 28 highly potent hydrocarbon molecules with high net heat of combustion, high specific impulse, high density and low melting point has been identified. The as-discovered molecules possess distinctive ring composition and unique spatial structure, which direct the synthetic efforts toward next generation HEDH fuels. This strategy not only discovers a new group of polycyclic molecules as competitive fuel candidates but also accelerates the development of new HEDH fuels.

机译：迫切需要下一代高能密度碳氢化合物（HEDH）燃料，以扩大推进系统的范围，并满足新发动机的其他要求。我们开发了一种基于机器学习的简便高效的方法，该方法可实现高通量筛选，从而加快下一代燃料的设计，并提供用于发现新型HEDH燃料的概念验证研究。此方法使用燃料的关键特性作为阈值来筛选319,895个碳氢化合物分子，并且已鉴定出28个具有高净燃烧热，高比冲，高密度和低熔点的高效碳氢化合物分子。如此发现的分子具有独特的环组成和独特的空间结构，将合成努力引向了下一代HEDH燃料。该策略不仅发现了一组新的多环分子作为竞争性候选燃料，而且还加快了新的HEDH燃料的开发。

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《Fuel》 |2020年第1期|116968.1-116968.7|共7页
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Tianjin Univ Sch Chem Engn & Technol Key Lab Green Chem Technol Minist Educ Tianjin 300072 Peoples R China|Collaborat Innovat Ctr Chem Sci & Engn Tianjin Tianjin 300072 Peoples R China;

Tianjin Univ Sch Chem Engn & Technol Key Lab Green Chem Technol Minist Educ Tianjin 300072 Peoples R China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Machine learning; Fuel; High-throughput screening; Hydrocarbon; DFT; Group-contribution;

机译：机器学习;汽油;高通量筛选;烃;DFT;小组贡献;

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Machine learning enabled high-throughput screening of hydrocarbon molecules for the design of next generation fuels

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