{"title":"Using Physical Property Models and Artificial Intelligence to Design Chemical Products","authors":"Kevin G. Joback","doi":"10.1016/j.fluid.2025.114441","DOIUrl":null,"url":null,"abstract":"<div><div>Designing chemical products is the process of combining pieces, e.g., molecular fragments or ingredients, into an assembly, e.g., chemical structures or mixture formulations, whose properties satisfy a set of design constraints. We explain how computational techniques, specifically artificial intelligence techniques, can greatly assist this design process. We demonstrate how proper representation of knowledge is essential for enabling the computer to manipulate substructures, how combinatorial algorithms are used to generate structures and enumerate isomers, how rule-based systems help select the estimation techniques needed to test each generated candidate chemical, and how machine learning can be used to improve the models used to find promising candidates.</div></div>","PeriodicalId":12170,"journal":{"name":"Fluid Phase Equilibria","volume":"596 ","pages":"Article 114441"},"PeriodicalIF":2.7000,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fluid Phase Equilibria","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378381225001116","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/4/11 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Designing chemical products is the process of combining pieces, e.g., molecular fragments or ingredients, into an assembly, e.g., chemical structures or mixture formulations, whose properties satisfy a set of design constraints. We explain how computational techniques, specifically artificial intelligence techniques, can greatly assist this design process. We demonstrate how proper representation of knowledge is essential for enabling the computer to manipulate substructures, how combinatorial algorithms are used to generate structures and enumerate isomers, how rule-based systems help select the estimation techniques needed to test each generated candidate chemical, and how machine learning can be used to improve the models used to find promising candidates.
期刊介绍：
Fluid Phase Equilibria publishes high-quality papers dealing with experimental, theoretical, and applied research related to equilibrium and transport properties of fluids, solids, and interfaces. Subjects of interest include physical/phase and chemical equilibria; equilibrium and nonequilibrium thermophysical properties; fundamental thermodynamic relations; and stability. The systems central to the journal include pure substances and mixtures of organic and inorganic materials, including polymers, biochemicals, and surfactants with sufficient characterization of composition and purity for the results to be reproduced. Alloys are of interest only when thermodynamic studies are included, purely material studies will not be considered. In all cases, authors are expected to provide physical or chemical interpretations of the results.
Experimental research can include measurements under all conditions of temperature, pressure, and composition, including critical and supercritical. Measurements are to be associated with systems and conditions of fundamental or applied interest, and may not be only a collection of routine data, such as physical property or solubility measurements at limited pressures and temperatures close to ambient, or surfactant studies focussed strictly on micellisation or micelle structure. Papers reporting common data must be accompanied by new physical insights and/or contemporary or new theory or techniques.
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