  Mimicking the perceptual functions of human cutaneous mechanoreceptors,
artificial skins or flexible pressure sensors can transduce tactile stimuli to
quantitative electrical signals. Conventional methods to design such devices
follow a forward structure-to-property routine based on trial-and-error
experiments/simulations, which take months or longer to determine one solution
valid for one specific material. Target-oriented inverse design that shows far
higher output efficiency has proven effective in other fields, but is still
absent for artificial skins because of the difficulties in acquiring big data.
Here, we report a property-to-structure inverse design of artificial skins
based on small dataset machine learning, exhibiting a comprehensive efficiency
at least four orders of magnitude higher than the conventional routine. The
inverse routine can predict hundreds of solutions that overcome the intrinsic
signal saturation problem for linear response in hours, and the solutions are
valid to a variety of materials. Our results demonstrate that the inverse
design allowed by small dataset is an efficient and powerful tool to target
multifarious applications of artificial skins, which can potentially advance
the fields of intelligent robots, advanced healthcare, and human-machine
interfaces.
