Abstract
The concept of the mechanophore1 was only established in the past two decades, during which time their discovery and study have led to new insights and opportunities in fundamental chemical reactivity2, imaging3, drug delivery4, and molecularly guided material properties5,6. Despite many successes in mechanophore design, the plausible design space in the field still far exceeds what has been realized synthetically. For example, ferrocenes are particularly attractive targets as mechanophores due to their combination of high thermal stability and mechanochemical lability.7,8 However, the handful of demonstrated ferrocene mechanophores is sparse7–9 in comparison to several thousands of unique ferrocene complexes that have been synthesized. Herein, we report computational, machine learning guided discovery of synthesizable ferrocene mechanophores. We identify over one hundred potential target ferrocene mechanophores with wide-ranging mechanochemical activity and use data-driven computational screening to identify a select number of promising complexes. We highlight design principles to alter mechanochemical activation of ferrocenes, including regio-controlled transition state stabilization through sterically bulky groups and a change in mechanism through non-covalent ligand–ligand interactions. The computational screening is validated experimentally both at the polymer strand level through sonication experiments and at the network level by mechanical testing. These experiments indicate that a computationally discovered ferrocene mechanophore cross-linker leads to greater than 4-fold enhancement in material tearing energy. We expect the computational approach to serve as a blueprint for high-throughput discovery in other families of mechanophores by providing fundamental insights into mechanically coupled reactivity, supporting the elucidation of mechanophore-to-material structure–activity relationships, and leading to polymer networks with new combinations of desired material properties.