  A key feature of mechanical structures ranging from crumple zones in cars to
padding in packaging is their ability to provide protection by absorbing
mechanical energy. Designing structures to efficiently meet these needs has
profound implications on safety, weight, efficiency, and cost. Despite the wide
varieties of systems that must be protected, a unifying design principle is
that protective structures should exhibit a high energy-absorbing efficiency,
or that they should absorb as much energy as possible without mechanical
stresses rising to levels that damage the system. However, progress in
increasing the efficiency of such structures has been slow due to the need to
test using tedious and manual physical experiments. Here, we overcome this
bottleneck through the use of a self-driving lab to perform >25,000 machine
learning-guided experiments in a parameter space with at minimum trillions of
possible designs. Through these experiments, we realized the highest mechanical
energy absorbing efficiency recorded to date. Furthermore, these experiments
uncover principles that can guide design for both elastic and plastic classes
of materials by incorporating both geometry and material into a single model.
This work shows the potential for sustained operation of self-driving labs with
a strong human-machine collaboration.
