How Chemical Structure Determines Physical, Chemical, and Technological Properties:  An Overview Illustrating the Potential of Quantitative Structure−Property Relationships for Fuels Science

2005 ◽  
Vol 19 (3) ◽  
pp. 922-935 ◽  
Author(s):  
Alan R. Katritzky ◽  
Dan C. Fara
Keyword(s):  
Chemical Structure ◽  
Quantitative Structure ◽  
Technological Properties ◽  
Structure Property ◽  
Structure Property Relationships ◽  
Physical Chemical ◽  
Quantitative Structure Property Relationships

Characteristic study of irregularity measures of some nanotubes

2019 ◽  
Vol 97 (10) ◽  
pp. 1125-1132 ◽  
Author(s):  
Zahid Iqbal ◽  
Adnan Aslam ◽  
Muhammad Ishaq ◽  
Muhammad Aamir
Keyword(s):  
Quantitative Structure ◽  
Structure Property ◽  
Know How ◽  
Molecular Graphs ◽  
Irregularity Index ◽  
Structure Property Relationships ◽  
Boiling Points ◽  
Quantitative Structure Property Relationships ◽  
Quantitative Structure Activity Relationships ◽  
Vertex Degrees

In many applications and problems in material engineering and chemistry, it is valuable to know how irregular a given molecular structure is. Furthermore, measures of the irregularity of underlying molecular graphs could be helpful for quantitative structure property relationships and quantitative structure-activity relationships studies, and for determining and expressing chemical and physical properties, such as toxicity, resistance, and melting and boiling points. Here we explore the following three irregularity measures: the irregularity index by Albertson, the total irregularity, and the variance of vertex degrees. Using graph structural analysis and derivation, we compute the above-mentioned irregularity measures of several molecular graphs of nanotubes.

Aqueous-phase photooxygenation of enes, amines, sulfides and polycyclic aromatics by singlet (a1Δg) oxygen: prediction of rate constants using orbital energies, substituent factors and quantitative structure–property relationships

2017 ◽  
Vol 14 (7) ◽  
pp. 442 ◽  
Author(s):  
Tom M. Nolte ◽  
Willie J. G. M. Peijnenburg
Keyword(s):  
Singlet Oxygen ◽  
Molecular Orbital ◽  
Organic Solvents ◽  
Aqueous Phase ◽  
Rate Constant ◽  
Rate Constants ◽  
Quantitative Structure ◽  
Structure Property ◽  
Structure Property Relationships ◽  
Quantitative Structure Property Relationships

Environmental contextTo aid the transition to sustainable chemistry there is a need to improve the degradability of chemicals and limit the use of organic solvents. Singlet oxygen, 1O2, is involved in organic synthesis and photochemical degradation; however, information on its aqueous-phase reactivity is limited. We developed cheminformatics models for photooxidation rate constants that will enable accurate assessment of aquatic photochemistry without experimentation. AbstractTo aid the transition to sustainable and green chemistry there is a general need to improve the degradability of chemicals and limit the use of organic solvents. In this study we developed quantitative structure–property relationships (QSPRs) for aqueous-phase photochemical reactions by singlet (a1Δg) oxygen. The bimolecular singlet oxygen reaction rate constant can be reliably estimated (R2 = 0.73 for naphtalenes and anthracenes, R2 = 0.86 for enes and R2 = 0.88 for aromatic amines) using the energy of the highest occupied molecular orbital (EHOMO). Additional molecular descriptors were used to characterise electronic and steric factors influencing the rate constant for aromatic enes (R2 = 0.74), sulfides and thiols (R2 = 0.72) and aliphatic amines. Mechanistic principles (frontier molecular orbital, perturbation and transition state theories) were applied to interpret the QSPRs developed and to corroborate findings in the literature. Depending on resonance, the speciation state (through protonation and deprotonation) can heavily influence the oxidation rate constant, which was accurately predicted. The QSPRs can be applied in synthetic photochemistry and for estimating chemical fate from photolysis or advanced water treatment.

scholarly journals Predicting permeability via statistical learning on higher-order microstructural information

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Magnus Röding ◽  
Zheng Ma ◽  
Salvatore Torquato
Keyword(s):  
Neural Networks ◽  
Physical Properties ◽  
Specific Surface ◽  
Correlation Functions ◽  
Quantitative Structure ◽  
Structure Property ◽  
Complex Materials ◽  
Structure Property Relationships ◽  
Quantitative Structure Property Relationships ◽  
Point Correlation

Abstract Quantitative structure–property relationships are crucial for the understanding and prediction of the physical properties of complex materials. For fluid flow in porous materials, characterizing the geometry of the pore microstructure facilitates prediction of permeability, a key property that has been extensively studied in material science, geophysics and chemical engineering. In this work, we study the predictability of different structural descriptors via both linear regressions and neural networks. A large data set of 30,000 virtual, porous microstructures of different types, including both granular and continuous solid phases, is created for this end. We compute permeabilities of these structures using the lattice Boltzmann method, and characterize the pore space geometry using one-point correlation functions (porosity, specific surface), two-point surface-surface, surface-void, and void-void correlation functions, as well as the geodesic tortuosity as an implicit descriptor. Then, we study the prediction of the permeability using different combinations of these descriptors. We obtain significant improvements of performance when compared to a Kozeny-Carman regression with only lowest-order descriptors (porosity and specific surface). We find that combining all three two-point correlation functions and tortuosity provides the best prediction of permeability, with the void-void correlation function being the most informative individual descriptor. Moreover, the combination of porosity, specific surface, and geodesic tortuosity provides very good predictive performance. This shows that higher-order correlation functions are extremely useful for forming a general model for predicting physical properties of complex materials. Additionally, our results suggest that artificial neural networks are superior to the more conventional regression methods for establishing quantitative structure–property relationships. We make the data and code used publicly available to facilitate further development of permeability prediction methods.

Checking the Efficacy of Two Basic Descriptors With a Set of Properties of Alkanes

2019 ◽  
Vol 4 (1) ◽  
pp. 67-87
Author(s):  
Emili Besalú ◽  
Riccardo Zanni ◽  
Lionello Pogliani ◽  
Jesus Vicente de Julian-Ortiz
Keyword(s):  
In Silico ◽  
Cross Validation ◽  
Model Building ◽  
Quantitative Structure ◽  
Structure Property ◽  
Structure Property Relationships ◽  
Building Activity ◽  
Quantitative Structure Property Relationships ◽  
The Cross ◽  
Residual Plots

Several experimental properties of alkanes are described by means of multilinear models at the cross-validation level. The models have been obtained considering two main sets of descriptors: mathematically-based and experimental ones. The best models are obtained normally involving one of the two sets. The main goal of this work is to show how the theoretical descriptors are able to perform a competitive role against the experimental ones. This constitutes an important topic in the quantitative structure-property relationships field because the use of mathematical and in silico descriptors is validated as a proper tool for model building. Activity distributions of the properties and indices employed are discussed, along with the shape of the obtained residual plots.

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