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Reliable Mechanochemistry: Protocols for Reproducible Outcomes of Neat and Liquid Assisted Ball-mill Grinding Experiments

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Modeling Targeted Mechanochemistry in Polymeric Solids.

Brandon C Jeong¹, Antonia Statt²

¹Department of Chemical Engineering, University of Illinois, Urbana-Champaign, Urbana, Illinois 61801, United States.

Chemical Reviews

|May 12, 2026

Summary

This summary is machine-generated.

This review covers computational modeling of mechanophores in polymers. Advancing these models will accelerate the discovery and design of new mechanoresponsive materials for various applications.

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Area of Science:

Materials Science
Polymer Chemistry
Computational Modeling

Background:

Mechanophores are molecules embedded in polymers that respond to mechanical force.
These materials have potential applications in sensing, self-healing, and adaptive systems.
Understanding mechanophore behavior is crucial for designing advanced materials.

Purpose of the Study:

To review and summarize various modeling approaches for mechanophores in polymer solids.
To compare computational strategies with experimental findings.
To identify challenges and future directions in the field.

Main Methods:

Quantum chemical models (nanoscale)
Reactive molecular dynamics (mesoscale)
Continuum frameworks (macroscale)
Theoretical foundations like force-modified potential energy surfaces

Main Results:

Key findings on the roles of mechanophore geometry, substituents, network architecture, and cross-linking in force transduction.
Comparison of computational predictions with experimental data.
Identification of factors influencing mechanophore activation.

Conclusions:

Multiscale modeling is essential for understanding mechanophore behavior.
Challenges remain in capturing complex dynamics, environmental effects, and heterogeneity.
Improved predictive models will drive the rational design of mechanoresponsive polymeric solids.