Good news!
"Artificial intelligence is helping to develop tougher plastics: Using stress-responsive molecules identified by a machine-learning model, MIT chemists created polymers that are more resistant to tearing. This could lead to more durable plastics and cut down on plastic waste."
"A new strategy for strengthening polymer materials could lead to more durable plastics and cut down on plastic waste ...
Using machine learning, the researchers identified crosslinker molecules that can be added to polymer materials, allowing them to withstand more force before tearing. These crosslinkers belong to a class of molecules known as mechanophores, which change their shape or other properties in response to mechanical force. ...
The crosslinkers that the researchers identified in this study are iron-containing compounds known as ferrocenes, which until now had not been broadly explored for their potential as mechanophores. Experimentally evaluating a single mechanophore can take weeks, but the researchers showed that they could use a machine-learning model to dramatically speed up this process. ..."
From the abstract:
"Mechanophores are molecules that undergo chemical changes in response to mechanical force, offering unique opportunities in chemistry, materials science, and drug delivery. However, many potential mechanophores remain unexplored.
For example, ferrocenes are attractive targets as mechanophores due to their combination of high thermal stability and mechanochemical lability. However, the mechanochemical potential of ferrocene derivatives remains dramatically underexplored despite the synthesis of thousands of structurally diverse complexes. Herein, we report the 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 their mechanochemical activation, including regio-controlled transition state stabilization through bulky groups and a change in mechanism through noncovalent ligand–ligand interactions.
The computational screening is validated experimentally both at the polymer strand level through sonication experiments and at the network level, where a computationally discovered ferrocene mechanophore cross-linker leads to greater than 4-fold enhancement in material tearing energy.
This work establishes a generalizable framework for the high-throughput discovery and rational design of mechanophores and offers insights into structure–activity relationships in mechanically responsive materials."
High-Throughput Discovery of Ferrocene Mechanophores with Enhanced Reactivity and Network Toughening (open access)
Graphical abstract
Figure 1. Screening procedure of the CSD curated data set of ferrocene complexes. Randomly sampled complexes are shown as cartoon renderings. Atoms are colored with hydrogen in white, carbon in gray, iron in dark orange, oxygen in red, nitrogen in blue, chlorine in green, and phosphorus in light orange.
Figure 3. Machine learning guided discovery of ferrocene mechanophores.
a, The maximum force distribution (violin plots) of the randomly selected unbridged (unbridged, green), randomly selected bridged (bridged, red) and machine learning selected (ML-unbridged, green) ferrocenes. ...
b, Classifier probability as a function of Fmax. Decision boundaries are shown as dashed lines. Correctly classified complexes are colored in green and incorrectly classified complexes are colored in red. ...
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