Distributive structure-properties relationship for flash point of multiple components mixture

Wang, Yali; Yan, Fangyou; Jia, Qi; Wang, Qiang

doi:10.1016/j.fluid.2018.07.005

Cited by 15 publications

(8 citation statements)

References 36 publications

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“…To accelerate the exploration of material design spaces, the material science community has recently paid much attention to machine learning-based data-driven approaches to the material design. Although most of molecular machine learning studies have focused on single-component systems, attempts have also been made to model properties of multicomponent molecular systems using datasets generated from massive computer simulations or systematic experiments. − However, it is rarely possible to obtain sufficient datasets for multicomponent materials due to the huge chemical space involved, and therefore, handling of a machine learning task on sparse datasets, which are produced on a trial-and-error basis during the process of the material design, is needed to extend the applicability of machine learning-based approaches for the multicomponent materials design. However, such a machine learning task is still challenging for current molecular machine learning models.…”

Section: Introductionmentioning

confidence: 99%

“…However, most of the molecular DNN models currently proposed are essentially for single-component systems and cannot be directly applied to multicomponent systems due to limitations in terms of inability to handle composition information and an arbitrary number of molecular structures. Therefore, most of existing approaches for machine learning of multicomponent molecular systems are still descriptor-based ,,− (see Figure d for typical cases). Another requirement for molecular DNN models in terms of application on multicomponent molecular systems is permutation invariance of input components, a mathematical property of a model whereby the order of components in its input is essentially independent of its output.…”

Section: Introductionmentioning

confidence: 99%

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Deep Neural Networks for Multicomponent Molecular Systems

Hanaoka

2020

ACS Omega

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Deep neural networks (DNNs) represent promising approaches to molecular machine learning (ML). However, their applicability remains limited to single-component materials and a general DNN model capable of handling various multicomponent molecular systems with composition data is still elusive, while current ML approaches for multicomponent molecular systems are still molecular descriptor-based. Here, a general DNN architecture extending existing molecular DNN models to multicomponent systems called MEIA is proposed. Case studies showed that the MEIA architecture could extend two exiting molecular DNN models to multicomponent systems with the same procedure, and that the obtained models that could learn both the molecular structure and composition information with equal or better accuracies compared to a well-used molecular descriptor-based model in the best model for each case study. Furthermore, the case studies also showed that, for ML tasks where the molecular structure information plays a minor role, the performance improvements by DNN models were small; while for ML tasks where the molecular structure information plays a major role, the performance improvements by DNN models were large, and DNN models showed notable predictive accuracies for an extremely sparse dataset, which cannot be modeled without the molecular structure information. The enhanced predictive ability of DNN models for sparse datasets of multicomponent systems will extend the applicability of ML in the multicomponent material design. Furthermore, the general capability of MEIA to extend DNN models to multicomponent systems will provide new opportunities to utilize the progress of actively developed single-component DNNs for the modeling of multicomponent systems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Multicomponent Molecular Systems

Hanaoka

2020

ACS Omega

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“…This approach allows the training on both data from pure compounds and fuels. The concept of weighted average descriptor has already been successfully applied for mixtures with only a few compounds, e.g., for the prediction of flash point. ,, We transfer this approach to jet fuels, mixtures of hundreds of possible molecules, with isomers that are not further identified by the GC × GC composition measurement.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Mean Quantitative Structure–Property Relationship Modeling of Jet Fuel Properties

et al. 2021

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We present a novel probabilistic mean quantitative structure–property relationship (M-QSPR) method for the prediction of jet fuel properties considering two-dimensional gas chromatography measurements. Fuels are represented as one mean pseudo-structure that is inferred by a weighted average over structures of 1866 molecules that could be present in the individual fuel. The method allows training of models on both data of pure components and of fuels and does not require mixing rules for the calculation of the bulk property. This drastically increases the number of available training data and allows the direct learning of the mixing behavior. For the modeling, we use a Monte-Carlo dropout neural network, a probabilistic machine learning algorithm, that estimates prediction uncertainties due to possible unidentified isomers and dissimilarity of training and test data. Models are developed to predict the freezing point, flash point, net heat of combustion, and temperature-dependent properties such as density, viscosity, and surface tension. We investigate the effect of the presence of fuels in the training data on the predictions for up to 82 conventional fuels and 50 synthetic fuels. The results of the predictions are compared on three metrics that quantify accuracy, precision, and reliability. These metrics allow a comprehensive estimation of the predictive capability of the models. For the prediction of density, surface tension, and net heat of combustion, the M-QSPR method yields highly accurate results even without the presence of fuels in the training data. For properties with nonlinear behavior over temperature and complex fuel component interactions, like viscosity and freezing point, the presence of fuels in the training data was found to be essential for the method.

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“…In many cases, QSPR techniques have been successfully used for the prediction of different properties for pure chemicals, which have been extensively reviewed elsewhere [ 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. However, only a few studies have been completed on QSPR models for mixtures [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ], due to the complexity of the structure description of the mixtures. In most studies, the molecular descriptors for each pure chemical were combined by mole-weighted averaging to derive mixture descriptors.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Lower Flammability Limits for Binary Hydrocarbon Gases by Quantitative Structure—Property Relationship Approach

Pan

Ding

et al. 2019

Molecules

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The lower flammability limit (LFL) is one of the most important parameters for evaluating the fire and explosion hazards of flammable gases or vapors. This study proposed quantitative structure−property relationship (QSPR) models to predict the LFL of binary hydrocarbon gases from their molecular structures. Twelve different mixing rules were employed to derive mixture descriptors for describing the structures characteristics of a series of 181 binary hydrocarbon mixtures. Genetic algorithm (GA)-based multiple linear regression (MLR) was used to select the most statistically effective mixture descriptors on the LFL of binary hydrocarbon gases. A total of 12 multilinear models were obtained based on the different mathematical formulas. The best model, issued from the norm of the molar contribution formula, was achieved as a six-parameter model. The best model was then rigorously validated using multiple strategies and further extensively compared to the previously published model. The results demonstrated the robustness, validity, and satisfactory predictivity of the proposed model. The applicability domain (AD) of the model was defined as well. The proposed best model would be expected to present an alternative to predict the LFL values of existing or new binary hydrocarbon gases, and provide some guidance for prioritizing the design of safer blended gases with desired properties.

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Distributive structure-properties relationship for flash point of multiple components mixture

Cited by 15 publications

References 36 publications

Deep Neural Networks for Multicomponent Molecular Systems

Deep Neural Networks for Multicomponent Molecular Systems

Probabilistic Mean Quantitative Structure–Property Relationship Modeling of Jet Fuel Properties

Prediction of Lower Flammability Limits for Binary Hydrocarbon Gases by Quantitative Structure—Property Relationship Approach

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