QSPR models for enthalpy and entropy of organic compounds based on a set of norm indices

Yan, Fangyou; Zhang, Y.; Niu, Huanqing; Xu, Feng; Xiong, Jialiang; Jia, Qi; Wang, Qiang

doi:10.1016/j.fluid.2023.113869

Cited by 2 publications

(1 citation statement)

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“…Similar to most machine learning models, the size and quality of the data are keys to building QSPR models. There are generally three main sources of molecular data: experimental measurement, theoretical calculation and molecular dynamics (MD) simulation [20][21][22]. Data from experimental measurements are generally recorded in databases or handbooks, and are the most frequently used in establishing QSPR models.…”

Section: Introductionmentioning

confidence: 99%

MDs-NP: a property prediction model construction procedure for naphtha based on molecular dynamics simulation

Wei,

Qiu

2024

J. Phys.: Condens. Matter

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In the context of carbon neutrality and carbon peaking, molecular management has become a focus of the petrochemical industry. The key to achieving molecular management is molecular reconstruction, which relies on rapid and accurate calculation of oil properties. Focusing on naphtha, we proposed a novel property prediction model construction procedure (MDs-NP) employing molecular dynamics simulations for property collections and gamma distribution from real analytical data for calculating mole fractions of simulation mixtures. We calculated 348 sets of mixture properties data in the range of 273K to 300K by molecular dynamics simulations. Molecular feature extraction was based on molecular descriptors. In addition to descriptors based on open-source toolkits (RDKit and Mordred), we designed 12 naphtha knowledge (NK) descriptors with a focus on naphtha. Three machine learning algorithms (support vector regression, extreme gradient boosting and artificial neural network) were applied and compared to establish models for the prediction of the density and viscosity of naphtha. Mordred and NK descriptors + support vector regression algorithm achieved the best performance for density. The selected RDKFp and NK descriptors + artificial neural network algorithm achieved the best performance for viscosity. Using ablation studies, T, P_w and CC(C)C are three effective descriptors in NK that can improve the performance of the property prediction models. MDs–NP has the potential to be extended to more properties as well as more-complex petroleum systems. The models from MDs–NP can be used for rapid molecular reconstruction to facilitate construction of data-driven models and intelligent transformation of petrochemical processes.

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Section: Introductionmentioning

confidence: 99%