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Related Concept Videos

Radical Reactivity: Overview01:11

Radical Reactivity: Overview

Radicals, the highly reactive species, gain stability by undergoing three different reactions. The first reaction involves a radical-radical coupling, in which a radical combines with another radical, forming a spin‐paired molecule. The second reaction is between a radical and a spin‐paired molecule, generating a new radical and a new spin‐paired molecule. The third reaction is radical decomposition in a unimolecular reaction, forming a new radical and a spin‐paired molecule. These three...
Radical Reactivity: Steric Effects01:10

Radical Reactivity: Steric Effects

The presence of electron-donating, electron-withdrawing, or conjugating groups adjacent to a radical center, imparts electronic stabilization to the radicals. Examples of such electronically-stabilized radicals are triphenylmethyl, tetramethylpiperidine‐N‐oxide, and 2,2‐diphenyl‐1‐picrylhydrazyl. These radicals are remarkably stable and are known as persistent radicals. Some of the persistent radicals can even be isolated and purified.
Along with electronic factors, steric factors also account...
Radical Reactivity: Nucleophilic Radicals01:16

Radical Reactivity: Nucleophilic Radicals

Radicals adjacent to electron-donating groups are called nucleophilic radicals. These radicals readily react with electrophilic alkenes. The SOMO–LUMO interactions are the driving force for the reaction, where the high-energy SOMO of the electron-rich, nucleophilic radicals interacts with the low-energy LUMO of the electron-deficient, electrophilic alkenes. Such SOMO–LUMO interactions are the basis of reactive radical traps, affecting the selectivity in radical reactions. For instance, consider...
Radical Reactivity: Electrophilic Radicals01:02

Radical Reactivity: Electrophilic Radicals

Radicals adjacent to electron‐withdrawing groups are called electrophilic radicals. These radicals readily react with nucleophilic alkenes. For example, the malonate radical, in which the radical center is flanked by two electron‐withdrawing groups, reacts readily with butyl vinyl ether, which consists of an electron‐donating oxygen substituent. The reaction between electrophilic malonate radical and nucleophilic vinyl ether is favored because the radical has a low‐energy SOMO, which interacts...
Radical Reactivity: Concentration Effects01:20

Radical Reactivity: Concentration Effects

In a radical reaction, the concentration of starting materials governs the selectivity of a radical. For example, the reaction between an alkyl halide and an alkene, in the presence of tin hydride and AIBN, begins with the generation of a tin radical. The generated radical then abstracts halogen from the alkyl halide, producing an alkyl radical. This alkyl radical can either react with tin hydride, yielding an alkane, or add to an alkene, generating a nitrile-stabilized radical, eventually...
Radical Reactivity: Intramolecular vs Intermolecular01:33

Radical Reactivity: Intramolecular vs Intermolecular

Radical reactions can occur either intermolecularly or intramolecularly. In an intermolecular radical reaction, a nucleophilic radical adds to an electrophilic alkene or vice versa. In such reactions, the radical and generally the alkene, which is also called the radical trap, are two different molecules. Additionally, for such intermolecular reactions to occur, the radical trap must be active, present in an excess concentration, and the radical starting material must have a weak carbon–halogen...

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  3. Multi-objective Optimization Of Conceptual Dft Reactivity Descriptors In Open-shell Radicals By Reinforcement Learning.
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  3. Multi-objective Optimization Of Conceptual Dft Reactivity Descriptors In Open-shell Radicals By Reinforcement Learning.

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Multi-Objective Optimization of Conceptual DFT Reactivity Descriptors in Open-Shell Radicals by Reinforcement

Debojyoti Das1, Preeti Christina Beck2, Debdutta Chakraborty2

  • 1Department of Chemistry, Louisiana State University, Baton Rouge, Louisiana 70803, United States.

Journal of Chemical Theory and Computation
|June 18, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study uses reinforcement learning to tune the reactivity of open-shell organic radicals for catalysis and materials science. The framework successfully balances global electrophilicity with local Fukui function behavior in diverse radical systems.

Related Experiment Videos

Area of Science:

  • Computational Chemistry
  • Materials Science
  • Organic Chemistry

Background:

  • Open-shell organic radicals are crucial for catalysis, energy materials, and spintronics.
  • Rational design of radicals is challenging due to difficulties in tuning electronic reactivity while maintaining local chemical response.

Purpose of the Study:

  • To develop a descriptor-driven reinforcement learning framework for regulating radical reactivity.
  • To achieve targeted electrophilicity (ω ≈ 1.0 eV) while ensuring admissible atom-condensed Fukui function behavior.

Main Methods:

  • Utilized a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm.
  • Employed conceptual density functional theory (CDFT) based reactivity indices, specifically electrophilicity index (ω) and atom-condensed Fukui functions.
  • Implemented a multiobjective optimization approach with benchmark tolerances.

    • Main Results:

    • Achieved an 85.7% success rate on held-out radicals using the TD3 framework.
    • Top-ranked candidates satisfied both global electrophilicity and local Fukui function criteria.
    • Identified electronic flexibility in scaffolds (e.g., phosphoryl, silyl) as key to successful tuning, while rigid motifs showed resistance.

    Conclusions:

    • Demonstrated that electronic flexibility dictates the tunability of open-shell radical reactivity descriptors.
    • Established a practical strategy for radical selection and descriptor-space optimization using interpretable electronic metrics.
    • Highlighted the necessity of coupling global electrophilicity control with local Fukui function regularization for robust optimization.