Group contribution-based property modeling for chemical product design: A perspective in the AI era

被引：30

作者：

Mann, Vipul ^{[1
]}

Gani, Rafiqul ^{[2
,3
]}

Venkatasubramanian, Venkat ^{[1
]}

机构：

[1] Columbia Univ, Dept Chem Engn, New York, NY 10027 USA

[2] PSE SPEED Co, Ordrup Jagtvej 42D, DK-2920 Charlottenlund, Denmark

[3] Hong Kong Univ Sci & Technol Guangzhou, Sustainable Energy & Environm Thrust, Guangzhou, Peoples R China

来源：

FLUID PHASE EQUILIBRIA | 2023年 / 568卷

基金：

美国国家科学基金会;

关键词：

Group contribution; Property prediction; Hybrid modeling; Artificial intelligence; Chemical product design; AIDED MOLECULAR DESIGN; PURE-COMPONENT PROPERTIES; DEEP NEURAL-NETWORKS; METHODOLOGY;

D O I：

10.1016/j.fluid.2023.113734

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

We provide a perspective of the challenges and opportunities for the group contribution approach for property prediction modeling with respect to their use in the design of chemical-based products in the modern era of artificial intelligence. In particular, we discuss issues related to the correct formulation of the product design problem, representation of molecular structures for property prediction as well as generation of product candidates, regression of property model parameters, and the integration of property related data and models with product design methods and tools using several conceptual examples. The need for developing appropriate hybrid AI models is described and recommendations for future work are presented.

引用

页数：17

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