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Reaction Quotient

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The status of a reversible reaction is conveniently assessed by evaluating its reaction quotient (Q). For a reversible reaction described by m A + n B ⇌ x C + y D, the reaction quotient is derived directly from the stoichiometry of the balanced equation as

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Related Experiment Video

Updated: Aug 15, 2025

Psychophysiological Assessment of the Effectiveness of Emotion Regulation Strategies in Childhood

Psychophysiological Assessment of the Effectiveness of Emotion Regulation Strategies in Childhood

Published on: February 11, 2017

The computational road to reactivity scales.

Maike Vahl¹, Jonny Proppe¹

¹Institute of Physical and Theoretical Chemistry, Technische Universität Braunschweig, Gaußstraße 17, 38106 Braunschweig, Germany. j.proppe@tu-braunschweig.de.

Physical Chemistry Chemical Physics : PCCP

|January 6, 2023

Summary

Chemists can efficiently predict molecular reactivity using quantum chemistry and machine learning. This approach generates quantitative structure-reactivity relationships, saving time and resources compared to traditional methods.

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Area of Science:

Computational chemistry
Machine learning
Chemical reactivity

Background:

Quantitative structure-reactivity relationships (QSRRs) are valuable for chemists.
Determining a single molecule's reactivity requires extensive experimental measurements.
This process is often time-consuming and resource-intensive.

Purpose of the Study:

To present efficient alternative approaches for generating QSRRs.
To highlight methods based on quantum chemistry and supervised learning.
To discuss the role of uncertainty quantification in these models.

Main Methods:

Utilizing quantum chemistry calculations.
Applying supervised learning algorithms.
Incorporating uncertainty quantification techniques.

Main Results:

Developed efficient methods for generating QSRRs.
Demonstrated the increasing predictability of these relationships over time.
Observed a trend towards more interpretable models.

Conclusions:

Quantum chemistry and machine learning offer efficient alternatives for reactivity prediction.
QSRRs are becoming more predictive and interpretable.
These methods reduce the need for extensive experimental measurements.