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Related Experiment Video

Updated: Jul 9, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Partition theory: a very simple illustration.

Morrel H Cohen¹, Adam Wasserman, Kieron Burke

¹Department of Physics and Astronomy, Rutgers University, 126 Frelinghuysen Road, Piscataway, New Jersey 08854, USA.

The Journal of Physical Chemistry. A

|December 7, 2007

Summary

This study introduces a new ensemble density functional theory method to rigorously divide molecular systems. The method successfully assigns finite chemical hardness to individual parts, overcoming limitations of previous theories.

Related Experiment Videos

Last Updated: Jul 9, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Area of Science:

Quantum Chemistry
Theoretical Chemistry
Computational Chemistry

Background:

Existing chemical reactivity theories struggle to define properties for isolated molecular fragments.
A rigorous method for partitioning complex molecular systems is needed.

Purpose of the Study:

To illustrate a novel ensemble density functional theory (eDFT) method for rigorously dividing molecular systems.
To demonstrate the calculation of chemical hardness for isolated molecular parts using eDFT.

Main Methods:

Application of a recently proposed ensemble density functional theory method.
Analysis of a hydrogen molecule analog system.
Derivation of analytic expressions for densities and the partition potential.

Main Results:

The eDFT method successfully divides a molecular system into its constituent parts.
Analytic expressions for the densities of the parts (hydrogen atoms) and the partition potential were derived.
Isolated parts were shown to possess finite and positive chemical hardness, unlike in previous theories.

Conclusions:

The proposed eDFT method provides a rigorous framework for molecular partitioning.
This approach successfully overcomes limitations in defining chemical hardness for isolated molecular fragments.
The method has significant implications for understanding chemical reactivity and molecular properties.