AIMC Topic: Solutions

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Accurate Simulations of Water and Aqueous Solutions through Fine-Tuned Dispersion-Corrected Density Functional Theory and Machine-Learning Interatomic Potentials.

Journal of chemical information and modeling
Dispersion-corrected density functional theory (DFT-D) is widely employed to model large molecular systems at an affordable computational cost and to develop machine-learning interatomic potentials (MLIPs), enabling reliable molecular dynamics (MD) s...

Ag+ modified paper-based SERS combined with SiPLS for quantitative detection of soluble As3+ in aqueous realgar solutions.

Analytica chimica acta
The detection of soluble arsenic in realgar and its preparations is crucial for toxicity evaluation. Therefore, surface-enhanced Raman spectroscopy (SERS) combined with machine learning was applied for the rapid detection of soluble As3+ in realgar a...

Ultraviolet-visible spectral characterization and ANN modeling of aqueous sugar solutions: Clinical and environmental perspectives.

PloS one
The characterization of aqueous sugar solutions using optical techniques offers a non-invasive, rapid, and reagent-free approach for concentration monitoring in both analytical and environmental contexts. In this study, aqueous D-glucose solutions at...

Physics-informed deep learning for plasmonic sensing of nanoscale protein dynamics in solution.

Science advances
Quantifying nanoscale protein secondary structure in aqueous solutions is crucial for understanding protein interactions and dynamics. Deep learning models are adept at predicting protein secondary structures, but their ability to model them in aqueo...

3D Spatial Learning for Adsorption Energy Prediction in Multi-Temporal Solution Systems: The MTSS Data Set and a GCN-Based Network.

Journal of chemical information and modeling
Existing methods for adsorption energy prediction primarily focus on individual molecules or static molecular pairs, lacking the capabilities to model the diverse spatial configurations found in complex solution systems. While traditional data sets a...

Prediction of thermodynamic properties of aqueous carbohydrates solution using the PHSC and ANN models.

Scientific reports
In this work the Artificial Neural Network (ANN) and the Perturbed Hard Sphere Chain (PHSC) equation of state (EoS) have been utilized to estimate the osmotic coefficient, activity coefficient, and water activity of aqueous sugar solutions containing...

Melting Profile of DNA in Crowded Solution: Model-Based Study.

International journal of molecular sciences
Recent advances in molecular dynamics (MD) simulations and the introduction of artificial intelligence (AI) have resulted in a significant increase in accuracy for structure prediction. However, the cell is a highly crowded environment consisting of ...

Fast and Accurate Prediction of Tautomer Ratios in Aqueous Solution via a Siamese Neural Network.

Journal of chemical theory and computation
Tautomerization plays a critical role in chemical and biological processes, influencing molecular stability, reactivity, biological activity, and ADME-Tox properties. Many drug-like molecules exist in multiple tautomeric states in aqueous solution, c...

Predicting RNA structure and dynamics with deep learning and solution scattering.

Biophysical journal
Advanced deep learning and statistical methods can predict structural models for RNA molecules. However, RNAs are flexible, and it remains difficult to describe their macromolecular conformations in solutions where varying conditions can induce confo...

Design and synthesis of a new recyclable nanohydrogel based on chitosan for Deltamethrin removal from aqueous solutions: Optimization and modeling by RSM-ANN.

International journal of biological macromolecules
In this study, a new magnetic biocompatible hydrogel was synthesized as an adsorbent for Deltamethrin pesticide removal. The optimal conditions and adsorption process of Deltamethrin by chitosan/polyacrylic acid/FeO nanocomposite hydrogel was studied...