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Molecular Models

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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.

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The protons in unsubstituted alkanes are strongly shielded with chemical shifts below 1.8 ppm. Methine, methylene, and methyl protons appear at approximately 1.7, 1.2 and 0.7 ppm, while the proton signal from methane appears at 0.23 ppm. An electronegative substituent, such as chlorine, withdraws the electron density from the protons, increasing their chemical shift. Progressive substitution of the hydrogens in methane by chlorine shifts the proton signals increasingly downfield, to 3.05 ppm in...

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Equilibrium calculations for systems involving multiple equilibria are often complex. For example, to calculate the solubility of a sparingly soluble salt in an aqueous solution in the presence of a common ion, one must consider all the equilibria in this solution. Calculations for these systems can be complicated and tedious, so a systematic approach with a series of steps is often helpful. The process is detailed below.
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Chemical Reactions

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A balanced chemical equation provides the information of chemical formulas of the reactants and products involved in the chemical change. A reaction’s stoichiometry helps predict how much of the reactant is needed to produce the desired amount of product, or in some cases, how much product will be formed from a specific amount of the reactant.
The relative amounts of reactants and products represented in a balanced chemical equation are often referred to as stoichiometric amounts.

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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

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Related Experiment Video

Updated: Jul 4, 2025

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

Deep learning for complex chemical systems.

Wei Li¹, Guoqiang Wang¹, Jing Ma¹

¹Key Laboratory of Mesoscopic Chemistry of Ministry of Education, Institute of Theoretical and Computational Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, China.

National Science Review

|January 26, 2024

Summary

This summary is machine-generated.

Deep learning connects molecular fragment details to overall system properties. This enables advanced multi-scale simulations for chemical systems and reactions.

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Area of Science:

Computational chemistry
Materials science
Chemical physics

Background:

Traditional simulations struggle with multi-scale complexity.
Bridging local and global properties is a key challenge.

Purpose of the Study:

To demonstrate deep learning's capability in multi-scale simulations.
To link molecular fragment features to macroscopic system behavior.

Main Methods:

Utilizing deep learning architectures.
Developing methods to integrate local orbital information.
Applying models to complex chemical systems.

Main Results:

Deep learning successfully bridges local and global properties.
Accurate multi-scale simulations of chemical processes are enabled.
Enhanced predictive power for complex systems.

Conclusions:

Deep learning offers a powerful framework for multi-scale simulations.
This approach advances the study of complex chemical reactions.
Potential for broader applications in computational science.