MIT Uses AI To Accelerate the Discovery of New Materials for 3D Printing


Accelerated Discovery of New 3D Printing Materials

Researchers at MIT and BASF have developed a data-driven system that accelerates the process of discovering new 3D printing materials that have multiple mechanical properties. Credit: Courtesy of the researchers

A new machine-learning system costs less, generates less waste, and can be more innovative than manual discovery methods.

The growing popularity of 3D printing for manufacturing all sorts of items, from customized medical devices to affordable homes, has created more demand for new 3D printing materials designed for very specific uses.

To cut down on the time it takes to discover these new materials, researchers at Website Builder

“We think, for a number of applications, this would outperform the conventional method because you can rely more heavily on the optimization algorithm to find the optimal solution. You wouldn’t need an expert chemist on hand to preselect the material formulations,” Foshey says.

The researchers have created a free, open-source materials optimization platform called AutoOED that incorporates the same optimization algorithm. AutoOED is a full software package that also allows researchers to conduct their own optimization.

Making materials

The researchers tested the system by using it to optimize formulations for a new 3D printing ink that hardens when it is exposed to ultraviolet light.

They identified six chemicals to use in the formulations and set the algorithm’s objective to uncover the best-performing material with respect to toughness, compression modulus (stiffness), and strength.

Maximizing these three properties manually would be especially challenging because they can be conflicting; for instance, the strongest material may not be the stiffest. Using a manual process, a chemist would typically try to maximize one property at a time, resulting in many experiments and a lot of waste.

The algorithm came up with 12 top performing materials that had optimal tradeoffs of the three different properties after testing only 120 samples.

Foshey and his collaborators were surprised by the wide variety of materials the algorithm was able to generate, and say the results were far more varied than they expected based on the six ingredients. The system encourages exploration, which could be especially useful in situations when specific material properties can’t be easily discovered intuitively.

Faster in the future

The process could be accelerated even more through the use of additional automation. Researchers mixed and tested each sample by hand, but robots could operate the dispensing and mixing systems in future versions of the system, Foshey says.

Farther down the road, the researchers would also like to test this data-driven discovery process for uses beyond developing new 3D printing inks.

“This has broad applications across materials science in general. For instance, if you wanted to design new types of batteries that were higher efficiency and lower cost, you could use a system like this to do it. Or if you wanted to optimize paint for a car that performed well and was environmentally friendly, this system could do that, too,” he says.

Because it presents a systematic approach for identifying optimal materials, this work could be a major step toward realizing high performance structures, says Keith A. Brown, assistant professor in the Department of Mechanical Engineering at Boston University.

“The focus on novel material formulations is particularly encouraging as this is a factor that is often overlooked by researchers who are constrained by commercially available materials. And the combination of data-driven methods and experimental science allows the team to identify materials in an efficient manner. Since experimental efficiency is something with which all experimenters can identify, the methods here have a chance of motivating the community to adopt more data-driven practices,” he says.

Reference: “Accelerated discovery of 3D printing materials using data-driven multiobjective optimization” by Timothy Erps, Michael Foshey, Mina Konaković Luković, Wan Shou, Hanns Hagen Goetzke, Herve Dietsch, Klaus Stoll, Bernhard von Vacano and Wojciech Matusik, 15 October 2021, Science Advances.
DOI: 10.1126/sciadv.abf7435

The research was supported by BASF.


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