Group contribution methods to predict enthalpy of vaporization of aromatic and terpene ketones at 298.15 K

Fonseca, Luciana Aparecida Andrade Previato; Cremasco, Marco Aurélio

doi:10.1016/j.fluid.2021.113009

Cited by 10 publications

(6 citation statements)

References 12 publications

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“…However, simply summing the contribution of each component may widen the error bars for property prediction. To solve this, physical chemists proposed the classic group contribution (GC) method, which is traditionally used to predict thermodynamic properties. − However, the cost and difficulty of manual modeling limit its application in optical properties; here, the sophisticated interactions between different groups of the molecule matter. Driven by big data, deep learning converts the representation of sample features in the original space into a new representation through feature conversion stage by stage, which makes classification or prediction easier .…”

Section: Introductionmentioning

confidence: 99%

Group Contribution Method Supervised Neural Network for Precise Design of Organic Nonlinear Optical Materials

Fan,

Yuan,

Qian

et al. 2024

Precision Chemistry

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To rationalize the design of D-π-A type organic small-molecule nonlinear optical materials, a theory guided machine learning framework is constructed. Such an approach is based on the recognition that the optical property of the molecule is predictable upon accumulating the contribution of each component, which is in line with the concept of group contribution method in thermodynamics. To realize this, a Lewis-mode group contribution method (LGC) has been developed in this work, which is combined with the multistage Bayesian neural network and the evolutionary algorithm to constitute an interactive framework (LGC-msBNN-EA). Thus, different optical properties of molecules are afforded accurately and efficiently�by using only a small data set for training. Moreover, by employing the EA model designed specifically for LGC, structural search is well achievable. The origins of the satisfying performance of the framework are discussed in detail. Considering that such a framework combines chemical principles and data-driven tools, most likely, it will be proven to be rational and efficient to complete mission regarding structure design in related fields.

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Section: Introductionmentioning

confidence: 99%

Group Contribution Method Supervised Neural Network for Precise Design of Organic Nonlinear Optical Materials

Fan,

Yuan,

Qian

et al. 2024

Precision Chemistry

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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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“…Group contribution (GC) is a classic method that has been traditionally used in predicting thermodynamic properties [ 27 – 29 ]. However, the cost and difficulty of its manual modeling limit its development in complex spectral properties.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Spectroscopy Using a 2-Stage, Generalized Constituent Contribution Protocol

2023

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A corrected group contribution (CGC)–molecule contribution (MC)–Bayesian neural network (BNN) protocol for accurate prediction of absorption spectra is presented. Upon combination of BNN with CGC methods, the full absorption spectra of various molecules are afforded accurately and efficiently—by using only a small dataset for training. Here, with a small training sample (<100), accurate prediction of maximum wavelength for single molecules is afforded with the first stage of the protocol; by contrast, previously reported machine learning (ML) methods require >1,000 samples to ensure the accuracy of prediction. Furthermore, with <500 samples, the mean square error in the prediction of full ultraviolet spectra reaches <2%; for comparison, ML models with molecular SMILES for training require a much larger dataset (>2,000) to achieve comparable accuracy. Moreover, by employing an MC method designed specifically for CGC that properly interprets the mixing rule, the spectra of mixtures are obtained with high accuracy. The logical origins of the good performance of the protocol are discussed in detail. Considering that such a constituent contribution protocol combines chemical principles and data-driven tools, most likely, it will be proven efficient to solve molecular-property-relevant problems in wider fields.

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Group contribution methods to predict enthalpy of vaporization of aromatic and terpene ketones at 298.15 K

Cited by 10 publications

References 12 publications

Group Contribution Method Supervised Neural Network for Precise Design of Organic Nonlinear Optical Materials

Group Contribution Method Supervised Neural Network for Precise Design of Organic Nonlinear Optical Materials

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

Machine Learning Spectroscopy Using a 2-Stage, Generalized Constituent Contribution Protocol

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