Predictive Modeling of Critical Temperatures in Superconducting Materials

n this study, we have investigated quantitative relationships between critical temperaturesof superconductive inorganic materials and the basic physicochemical attributes of these materials(also called quantitative structure-property relationships). We demonstrated that one of the mostrecent studies (titled A data-driven statistical model for predicting the critical temperature of asuperconductor” and published in Computational Materials Science by K. Hamidieh in 2018) reportson models that were based on the dataset that contains 27% of duplicate entries. We aimed todeliver stable models for a properly cleaned dataset using the same modeling techniques (multiplelinear regression, MLR, and gradient boosting decision trees, XGBoost). The predictive ability ofour best XGBoost model (R2 = 0.924, RMSE = 9.336 using 10-fold cross-validation) is comparableto the XGBoost model by the author of the initial dataset (R2 = 0.920 and RMSE = 9.5 K in ten-foldcross-validation). At the same time, our best model is based on less sophisticated parameters, whichallows one to make more accurate interpretations while maintaining a generalizable model. Inparticular, we found that the highest relative influence is attributed to variables that represent thethermal conductivity of materials. In addition to MLR and XGBoost, we explored the potential ofother machine learning techniques (NN, neural networks and RF, random forest

Advancement of predictive modeling of zeta potentials (ζ) in metal oxide nanoparticles with correlation intensity index (CII)

It was expected that index of the ideality of correlation (IIC) and correlation intensity index (CII) could be used as possible tools to improve the predictive power of the quantitative model for zeta potential of nanoparticles. In this paper, we test how the statistical quality of quantitative structure-activity models for zeta potentials (ζ, a common measurement that reflects surface charge and stability of nanomaterial) could be improved with the use of these two indexes. Our hypothesis was tested using the benchmark data set that consists of 87 measurements of zeta potentials in water. We used quasi-SMILES molecular representation to take into consideration the size of nanoparticles in water and calculated optimal descriptors and predictive models based on the Monte Carlo method. We observed that the models developed with utilization of CII are statistically more reliable than models obtained with the IIC. However, the described approach gives an improvement of the statistical quality of these models for the external validation sets to the detriment for the training sets. Nevertheless, this circumstance is rather an advantage than a disadvantage

Modeling of Interactions between the Zebrafish Hatching Enzyme ZHE1 and A Series of Metal Oxide Nanoparticles: Nano-QSAR and Causal Analysis of Inactivation Mechanisms

The quantitative relationships between the activity of zebrafish ZHE1 enzyme and a series of experimental and physicochemical features of 24 metal oxide nanoparticles were revealed. Vital characteristics of the nanoparticles’ structure were reflected using both experimental and theoretical descriptors. The developed quantitative structure–activity relationship model for nanoparticles (nano-QSAR) was capable of predicting the enzyme inactivation based on four descriptors: the hydrodynamic radius, mass density, the Wigner–Seitz radius, and the covalent index. The nano-QSAR model was calculated using the non-linear regression tree M5P algorithm. The developed model is characterized by high robustness R2bagging = 0.90 and external predictivity Q2EXT = 0.93. This model is in agreement with modern theories of aquatic toxicity. Dissolution and size-dependent characteristics are among the key driving forces for enzyme inactivation. It was proven that ZnO, CuO, Cr2O3, and NiO nanoparticles demonstrated strong inhibitory effects because of their solubility. The proposed approach could be used as a non-experimental alternative to animal testing. Additionally, methods of causal discovery were applied to shed light on the mechanisms and modes of action

Predictive Modeling of Critical Temperatures in Superconducting Materials

In this study, we have investigated quantitative relationships between critical temperatures of superconductive inorganic materials and the basic physicochemical attributes of these materials (also called quantitative structure-property relationships). We demonstrated that one of the most recent studies (titled "A data-driven statistical model for predicting the critical temperature of a superconductor” and published in Computational Materials Science by K. Hamidieh in 2018) reports on models that were based on the dataset that contains 27% of duplicate entries. We aimed to deliver stable models for a properly cleaned dataset using the same modeling techniques (multiple linear regression, MLR, and gradient boosting decision trees, XGBoost). The predictive ability of our best XGBoost model (R2 = 0.924, RMSE = 9.336 using 10-fold cross-validation) is comparable to the XGBoost model by the author of the initial dataset (R2 = 0.920 and RMSE = 9.5 K in ten-fold cross-validation). At the same time, our best model is based on less sophisticated parameters, which allows one to make more accurate interpretations while maintaining a generalizable model. In particular, we found that the highest relative influence is attributed to variables that represent the thermal conductivity of materials. In addition to MLR and XGBoost, we explored the potential of other machine learning techniques (NN, neural networks and RF, random forests)

Modeling of Interactions between the Zebrafish Hatching Enzyme ZHE1 and A Series of Metal Oxide Nanoparticles: Nano-QSAR and Causal Analysis of Inactivation Mechanisms

The quantitative relationships between the activity of zebrafish ZHE1 enzyme and a series of experimental and physicochemical features of 24 metal oxide nanoparticles were revealed. Vital characteristics of the nanoparticles’ structure were reflected using both experimental and theoretical descriptors. The developed quantitative structure–activity relationship model for nanoparticles (nano-QSAR) was capable of predicting the enzyme inactivation based on four descriptors: the hydrodynamic radius, mass density, the Wigner–Seitz radius, and the covalent index. The nano-QSAR model was calculated using the non-linear regression tree M5P algorithm. The developed model is characterized by high robustness R2bagging = 0.90 and external predictivity Q2EXT = 0.93. This model is in agreement with modern theories of aquatic toxicity. Dissolution and size-dependent characteristics are among the key driving forces for enzyme inactivation. It was proven that ZnO, CuO, Cr2O3, and NiO nanoparticles demonstrated strong inhibitory effects because of their solubility. The proposed approach could be used as a non-experimental alternative to animal testing. Additionally, methods of causal discovery were applied to shed light on the mechanisms and modes of action

A DFT-based QSAR study on inhibition of human dihydrofolate reductase

Diaminopyrimidine derivatives are frequently used as inhibitors of human dihydrofolate reductase, for example in treatment of patients whose immune system are affected by human immunodeficiency virus. Forty-seven dicyclic and tricyclic potential inhibitors of human dihydrofolate reductase were analyzed using the quantitative structure-activity analysis supported by DFT-based and DRAGON-based descriptors. The developed model yielded an RMSE deviation of 1.1 a correlation coefficient of 0.81. The prediction set was characterized by R-2 = 0.60 and RMSE = 3.59. Factors responsible for inhibition process were identified and discussed. The resulting model was validated via cross validation and Y-scrambling procedure. From the best model, we found several mass-related descriptors and Sanderson electronegativity-related descriptors that have the best correlations with the investigated inhibitory concentration. These descriptors reflect results from QSAR studies based on characteristics of human dihydrofolate reductase inhibitors.National Science Foundation (NSF) - HRD-0833178National Science Foundation: EPSCoR Grant - 362492-190200-01 \NSFEPS- 090378

Towards the Development of Global Nano-Quantitative Structure–Property Relationship Models: Zeta Potentials of Metal Oxide Nanoparticles

Zeta potential indirectly reflects a charge of the surface of nanoparticles in solutions and could be used to represent the stability of the colloidal solution. As processes of synthesis, testing and evaluation of new nanomaterials are expensive and time-consuming, so it would be helpful to estimate an approximate range of properties for untested nanomaterials using computational modeling. We collected the largest dataset of zeta potential measurements of bare metal oxide nanoparticles in water (87 data points). The dataset was used to develop quantitative structure–property relationship (QSPR) models. Essential features of nanoparticles were represented using a modified simplified molecular input line entry system (SMILES). SMILES strings reflected the size-dependent behavior of zeta potentials, as the considered quasi-SMILES modification included information about both chemical composition and the size of the nanoparticles. Three mathematical models were generated using the Monte Carlo method, and their statistical quality was evaluated (R2 for the training set varied from 0.71 to 0.87; for the validation set, from 0.67 to 0.82; root mean square errors for both training and validation sets ranged from 11.3 to 17.2 mV). The developed models were analyzed and linked to aggregation effects in aqueous solutions

Binding of Alkali Metal Ions with 1,3,5-Tri(phenyl)benzene and 1,3,5-Tri(naphthyl)benzene: The Effect of Phenyl and Naphthyl Ring Substitution on Cation−π Interactions Revealed by DFT Study

The effect of substitution of phenyl and naphthyl rings to benzene was examined to elucidate the cation−π interactions involving alkali metal ions with 1,3,5-tri­(phenyl)­benzene (TPB) and 1,3,5-tri­(naphthyl)­benzene (TNB). Benzene, TPB, and four TNB isomers (with ααα, ααβ, αββ, and βββ types of fusion) and their complexes with Li⁺, Na⁺, K⁺, Rb⁺, and Cs⁺ were optimized using DFT approach with B3LYP and M06-2X functionals in conjunction with the def2-QZVP basis set. Higher relative stability of β,β,β-TNB over α,α,α-TNB can be attributed to <i>peri</i> repulsion, which is defined as the nonbonding repulsive interaction between substituents in the 1- and the 8-positions on the naphthalene core. Binding energies, distances between ring centroid and the metal ions, and the distance to metal ions from the center of other six-membered rings were compared for all complexes. Our computational study reveals that the binding affinity of alkali metal cations increases significantly with the 1,3,5-trisubstitution of phenyl and naphthyl rings to benzene. The detailed computational analyses of geometries, partial charges, binding energies, and ligand organization energies reveal the possibility of favorable C–H···M⁺ interactions when a α-naphthyl group exists in complexes of TNB structures. Like benzene-alkali metal ion complexes, the binding affinity of metal ions follows the order: Li⁺ > Na⁺ > K⁺ > Rb⁺ > Cs⁺ for any considered 1,3,5-trisubstituted benzene systems. In case of TNB, we found that the strength of interactions increases as the fusion point changes from α to β position of naphthalene

How toxicity of nanomaterials towards different species could be simultaneously evaluated: Novel multi-nano-read-across approach

Application of predictive modeling approaches is able solve the problem of the missing data. There are a lot of studies that investigate the effects of missing values on qualitative or quantitative modeling, but only few publications have beendiscussing it in case of applications to nanotechnology related data. Current project aimed at the development of multi-nano-read-across modeling technique that helps in predicting the toxicity of different species: bacteria, algae, protozoa, and mammalian cell lines. In this study, the experimental toxicity for 184 metal- and silica oxides (30 unique chemical types) nanoparticles from 15 experimental datasets was analyzed. A hybrid quantitative multi-nano-read-across approach that combines interspecies correlation analysis and self-organizing map analysis was developed. At the first step, hidden patterns of toxicity among the nanoparticles were identified using a combination of methods. Then the developed model that based on categorization of metal oxide nanoparticles’ toxicity outcomes was evaluated by means of combination of supervised and unsupervised machine learning techniques to find underlying factors responsible for toxicity

How the “Liquid Drop” Approach Could Be Efficiently Applied for Quantitative Structure–Property Relationship Modeling of Nanofluids

The main goal of this paper is the evaluation of the applicability of the geometrical “liquid drop” model (LDM) to describe physicochemical properties of nanofluids in quantitative structure–property relationship (QSPR) modeling. LDM-based descriptors are size-dependent, which allows them to be applied for a series of nanoparticles with the same chemical composition but different sizes. Thermal conductivity of nanofluids as the target property was investigated. Random forest regression as a nonparametric approach was utilized to determine important structural features of nanofluids responsible for enhancing their thermal conductivity