Quantitative Structure-Property Relationship (QSPR) models for Minimum Ignition Energy (MIE) prediction of combustible dusts using machine learning

Chaudhari, Purvali; Ade, Nilesh; Pérez, Lisa M.; Kolis, Stanley P.; Mashuga, Chad V.

doi:10.1016/j.powtec.2020.05.118

Cited by 17 publications

(5 citation statements)

References 30 publications

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“…In recent years, machine learning (ML) techniques have facilitated the application of quantum chemical methods for predicting molecular properties to address this computational challenge. Quantitative structure–property relationships (QSPR) derived by machine learning (ML) have screened properties of prospective drug molecules, , fuels, , and polymers. , These methods implicitly approximate quantum chemical calculations by a coarse-grained basis set . As such, the methods have low accuracy for predicting properties that depend on electronic structure, which is problematic because IP and σ are both highly sensitive to electronic structure.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Ionization Potentials and Photoionization Cross Sections of Volatile Organic Compounds

Stewart

Martin

2023

ACS Earth Space Chem.

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Molecular ionization potentials (IP) and photoionization cross sections (σ) can affect the sensitivity of photoionization detectors (PIDs) and other sensors for gaseous species. This study employs several methods of machine learning (ML) to predict IP and σ values at 10.6 eV (117 nm) for a dataset of 1251 gaseous organic species. The explicitness of the treatment of the species electronic structure progressively increases among the methods. The study compares the ML predictions of the IP and σ values to those obtained by quantum chemical calculations. The ML predictions are comparable in performance to those of the quantum calculations when evaluated against measurements. Pretraining further reduces the mean absolute errors (ε) compared to the measurements. The graph-based attentive fingerprint model was most accurate, for which ε IP = 0.23 ± 0.01 eV and ε σ = 2.8 ± 0.2 Mb compared to measurements and computed cross sections, respectively. The ML predictions for IP correlate well with both the measured IPs (R 2 = 0.88) and with IPs computed at the level of M06-2X/aug-cc-pVTZ (R 2 = 0.82). The ML predictions for σ correlated reasonably well with computed cross sections (R 2 = 0.66). The developed ML methods for IP and σ values, representing the properties of a generalizable set of volatile organic compounds (VOCs) relevant to industrial applications and atmospheric chemistry, can be used to quantitatively describe the species-dependent sensitivity of chemical sensors that use ionizing radiation as part of the sensing mechanism, such as photoionization detectors.

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Section: Introductionmentioning

confidence: 99%

Machine Learning for Ionization Potentials and Photoionization Cross Sections of Volatile Organic Compounds

Stewart

Martin

2023

ACS Earth Space Chem.

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“…Quantitative Structure Property/Activity Relationship (QSPR/QSAR) models have been widely employed for several decades in chemistry-related fields to predict various endpoints of molecules (i.e., physico-chemical properties and biological activities, respectively) on the basis of their structure (e.g., descriptors, fingerprints, graphs), via mathematical methods. Successful QSPR/QSAR applications include very different endpoints such as critical temperature and pressure [1], normal boiling point [2], heat capacity [3], enthalpy of solvation [4]/vaporization [5,6], blood-brain barrier permeability [7], physico-chemical properties of polymers/fuels/ionic liquids [8][9][10][11][12][13][14][15], solubility [16][17][18][19][20][21], minimum ignition energy of combustible dusts [22] or antibacterial/antiviral properties [23,24].…”

Section: Introductionmentioning

confidence: 99%

On the Development of Descriptor-Based Machine Learning Models for Thermodynamic Properties: Part 1—From Data Collection to Model Construction: Understanding of the Methods and Their Effects

Trinh,

Tbatou,

Lasala

et al. 2023

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In the present work, a multi-angle approach is adopted to develop two ML-QSPR models for the prediction of the enthalpy of formation and the entropy of molecules, in their ideal gas state. The molecules were represented by high-dimensional vectors of structural and physico-chemical characteristics (i.e., descriptors). In this sense, an overview is provided of the possible methods that can be employed at each step of the ML-QSPR procedure (i.e., data preprocessing, dimensionality reduction and model construction) and an attempt is made to increase the understanding of the effects related to a given choice or method on the model performance, interpretability and applicability domain. At the same time, the well-known OECD principles for the validation of (Q)SAR models are also considered and addressed. The employed data set is a good representation of two common problems in ML-QSPR modeling, namely the high-dimensional descriptor-based representation and the high chemical diversity of the molecules. This diversity effectively impacts the subsequent applicability of the developed models to a new molecule. The data set complexity is addressed through customized data preprocessing techniques and genetic algorithms. The former improves the data quality while limiting the loss of information, while the latter allows for the automatic identification of the most important descriptors, in accordance with a physical interpretation. The best performances are obtained with Lasso linear models (MAE test = 25.2 kJ/mol for the enthalpy and 17.9 J/mol/K for the entropy). Finally, the overall developed procedure is also tested on various enthalpy and entropy related data sets from the literature to check its applicability to other problems and competing performances are obtained, highlighting that different methods and molecular representations can lead to good performances.

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“…These descriptors can be as simple as a list of molecule’s compositions 5 , 6 or as complex as matrices, 18 − 20 fingerprints, 15 , 16 , 21 etc. 8 , 14 , 22 − 26 …”

Section: Introductionmentioning

confidence: 99%

“…Some novel applications include generating drug candidates, investigating chemical phenomena, and assisting theoretical calculation. − One of the most prominent tasks for applying machine learning is physical or chemical property predictions. In this direction, quantitative structure–activity relationship (QSAR) specializes in the prediction of the biological activity of compounds from their structural information. − Similarly, materials scientists employ a similar technique called quantitative structure–property relationship (QSPR) for predicting various properties of materials from their 2D or 3D structural data. − In most QSPR and QSAR methodologies, predicting tasks heavily depend on a set of descriptors, , which serve as numerical representations of structural information of molecules. Typically, these descriptors are numerical objects obtained by transforming raw molecular data by some predefined procedure.…”

Section: Introductionmentioning

confidence: 99%

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Application of Machine Learning in Developing Quantitative Structure–Property Relationship for Electronic Properties of Polyaromatic Compounds

2022

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The degree of π orbital overlap (DPO) model has been demonstrated to be an excellent quantitative structure–property relationship (QSPR) that can map two-dimensional structural information of polycyclic aromatic hydrocarbons (PAHs) and thienoacenes to their electronic properties, namely, band gaps, electron affinities, and ionization potentials. However, the model suffers from significant limitations that narrow its applications due to inefficient manual procedures in parameter optimization and descriptor formulation. In this work, we developed a machine learning (ML)-based method for efficiently optimizing DPO parameters and proposed a truncated DPO descriptor, which is simple enough that can be automatically extracted from simplified molecular-input line-entry system strings of PAHs and thienoacenes. Compared with the result from our previous studies, the ML-based methodology can optimize DPO parameters with four times fewer data, while it can achieve the same level of accuracy in predictions of the mentioned electronic properties to within 0.1 eV. The truncated DPO model also has similar accuracy to the full DPO model. Consequently, the ML-based DPO approach coupled with the truncated DPO model enables new possibilities for developing automatic pipelines for high-throughput screening and investigating new QSPR for new chemical classes.

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Quantitative Structure-Property Relationship (QSPR) models for Minimum Ignition Energy (MIE) prediction of combustible dusts using machine learning

Cited by 17 publications

References 30 publications

Machine Learning for Ionization Potentials and Photoionization Cross Sections of Volatile Organic Compounds

Machine Learning for Ionization Potentials and Photoionization Cross Sections of Volatile Organic Compounds

On the Development of Descriptor-Based Machine Learning Models for Thermodynamic Properties: Part 1—From Data Collection to Model Construction: Understanding of the Methods and Their Effects

Application of Machine Learning in Developing Quantitative Structure–Property Relationship for Electronic Properties of Polyaromatic Compounds

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