3D printers that once could only produce rigid objects can now create products as soft and stretchable as rubber. A team of Korean researchers used AI to identify the optimal "recipe" for a material that can be printed into complex shapes while stretching to more than six times its original length. The material is expected to expand the range of applications for 3D printing, from robotic hands to form-fitting wearable devices and custom medical devices.

KAIST (President Choong-Sik Bae) announced on September 1 that a research team led by Professor Seungchul Lee from the Department of Mechanical Engineering, working with Dr. Jongbeom Na's team at the Korea Institute of Science and Technology’s (KIST, President Sang-Rok Oh) Extreme Materials Research Center and Professor Bumsoo Park from the Department of Manufacturing Systems and Design Engineering (MSDE) at Seoul National University of Science and Technology (SEOULTECH, President Dong-Hwan Kim), had used AI to develop a material that is both 3D-printable and highly stretchable, like rubber.

The need for such materials — soft, stretchable, and capable of forming complex shapes — has been growing as soft robots that come into direct contact with people, wearable devices worn on the body, and medical devices custom-fitted to patients have drawn increasing attention.

The 3D printing technology the team used, Digital Light Processing (DLP), cures a liquid material into a desired shape by exposing it to light. While DLP can quickly produce complex structures, making a material more stretchable and durable tends to raise its viscosity to the point that it no longer flows well enough to be printed. Conversely, thinning the material to make it easier to print reduces its stretchability and strength. Thus, developing a material that is both easy to print and highly stretchable was the central challenge.

The team used AI to identify the optimal "material recipe" that satisfies both conditions. Notably, the training data included not only materials that print well, but also highly viscous materials that are difficult to print.

The researchers cured various liquid material formulations in small molds and measured how stretchable and hard they were, how quickly they cured under light, and how well they flowed. This produced a dataset linking a wide range of material formulations to their respective properties.

The team then used machine learning to examine the relationship between material formulation and performance. Based on this, the AI identified the optimal material combination that is both 3D-printable and highly stretchable.

The material identified by the AI printed reliably on a DLP 3D printer and showed high stretchability, extending to more than six times its original length when pulled, without easily tearing.

To verify its real-world potential, the team 3D-printed a "soft actuator" using the material. A soft actuator is a device that uses air pressure and other means to create gentle, muscle-like movement. When inflated with air, it expanded like a balloon and bent as naturally as a human finger.

A soft robotic hand made by combining several actuators lifted a 1 kg water bottle and successfully and stably grasped objects of varying shapes and rigidity, from fragile eggs to glass bottles, an egg carton, and a computer mouse.

Beyond developing a single highly stretchable material, this research is significant for presenting an AI-based method for more quickly identifying materials with desired properties.

Previously, researchers had to directly formulate and test countless materials to find the optimal combination. Going forward, AI can first identify promising material combinations based on experimental data, which researchers then verify through testing, thereby reducing trial and error and shortening material development time.

"This research is significant as it shows that combining researchers' experimental data with artificial intelligence can efficiently identify optimal material combinations that were previously difficult to find," explained Professor Seungchul Lee. "We expect it to be used to more rapidly develop 3D-printing materials with the performance needed across a range of fields, including soft robots, wearable devices, and custom medical devices."

The study, with Dr. Younghan Song and Professor Bumsoo Park as co-first authors, was published in the international journal Nature Communications on June 4.

Paper title: "Machine learning guided formulation design of digital light processing printable elastomers beyond viscosity stretchability tradeoff"
DOI: https://doi.org/10.1038/s41467-026-73735-4

This research was supported by the Ministry of Trade, Industry and Resource's Machinery and Equipment Industry Technology Development Program (20023762), and by the Ministry of Science and ICT's Nano & Material Technology Development Program (RS-2026-25534767) and Excellent New Researcher Program (RS-2024-00350423).