Themed collection Machine learning and artificial neural networks in chemistry

Chemical artificial intelligent systems to face the challenges of complexity.

Adsorption coefficients of 39 aromatic compounds onto multi-walled carbon nanotubes have been compiled.

Review on predictive models for the estimation of thermophysical properties of ionic liquids with a critical assessment of the quality of the models and their areas of applicability.

The neuron model regulated by DNAzymes is simple to construct and possesses strong scalability, having great potential for use in the construction of large neural networks.

Rapid equilibration within a short time, high adsorption capacity, optimization, multivariate interaction of adsorption parameters and artificial neural network prediction model.

Structure characterization and classification is frequently based on local environment information of all or selected atomic sites in the crystal structure.

Development of a novel QSAR model for the prediction of toxicity of superparamagnetic iron oxide nanoparticles (SPIONs) in stem-cell monitoring applications.

Theoretical approaches are formulated to investigate the molecular mobility under various cooling rates of amorphous drugs.

A new tool for prediction the diameter of nanofibers is presented: the use of adaptive modeling techniques to predict fiber diameter and study the impact of electrospinning process parameters on electrospinning fiber diameter.

An artificial neutral network has been applied to predict the specific capacitance of biomass-carbon supercapacitors.

The excellent representation ability of deep spectral features enables hyperspectral imaging combined with deep convolutional neural network to be a powerful tool for large-scale seeds detection in modern seed industry.

Machine learning algorithms enhance the specificity of cancer biomarkers detection using SERS-based immunoassays.

In this study, we utilize a density functional theory-machine learning framework to develop a high-throughput screening method for designing new molecular electrode materials.

There is an exigency of transformation of the enormous amount of biological data available in various forms into some significant knowledge.

The d-band centers for eleven metals and their pairwise bimetals for two different structures (1% metal doped- or overlayer-covered metal surfaces) are statistically predicted using machine learning methods from readily available values as descriptors for the target metals.

This study is based on the usage of a composite of zinc sulfide nanoparticles with activated carbon (ZnS-NPs-AC) for the adsorption of methylene blue (MB) from aqueous solutions.

The present study deals with the simultaneous removal of chrysoidine G (CG), rhodamine B (RB) and disulfine blue (DB) by Ni doped ferric oxyhydroxide FeO(OH) nanowires on activated carbon (Ni doped FeO(OH)-NWs–AC).

The classification models were constructed to discover neuroprotective compounds against glutamate or H₂O₂-induced neurotoxicity through machine learning approaches.

MnO₂ nanoparticle-loaded activated carbon (MnO₂-NP-AC) as an efficient, environmental friendly and cost-effective adsorbent was synthesized and characterized using different techniques such as FE-SEM, EDX, XRD, BET and FTIR.

An experimental design was adopted to attain the optimum reaction parameters of chemical derivatization of anhydrous sodium alendronate in an oral solution formula via Hantzsch condensation reaction.