Despite being researched for decades, shape-shifting
molecular
crystals have yet to claim their spot as an actuating materials class
among the primary functional materials. While the process for developing
and commercializing materials can be lengthy, it inevitably starts
with building an extensive knowledge base, which for molecular crystal
actuators remains scattered and disjointed. Using machine learning
for the first time, we identify inherent features and structure–function
relationships that fundamentally impact the mechanical response of
molecular crystal actuators. Our model can factor in different crystal
properties in tandem and decipher their intersectional and combined
effects on each actuation performance. This analysis is an open invitation
to utilize interdisciplinary expertise in translating the current
basic research on molecular crystal actuators into technology-based
development that promotes large-scale experimentation and prototyping.