A partnership between the Massachusetts Institute of Technology and the chemical giant BASF has managed to successfully create an AI-driven process to speed up the discovery of custom 3D printing materials.
Chemists usually develop a few iterations of a material candidate over a couple of days and test them in the lab. The new machine-learning algorithm can churn out hundreds of those iterations with the desired characteristics in the same timeframe. This would save time and raw material costs, as well as lessen the environmental impact of the discarded chemicals. Not only that, but the algorithm may also come up with ideas that the material's engineer could have overlooked for various reasons. Their human intuition would then choose what makes the most sense from way more raw material combinations that fit certain requirements like durability, tensile, or compression strength.
Those material characteristics can often be mutually exclusive, so the MIT researchers used the algorithm to discover new 3D printing ink that would've otherwise taken a lot of experimenting to score the right balance. The inputs were six starting materials and a requirement that the ink bakes under UV light. The output was no less than 12 material candidates with the right characteristics for the job. The only thing left for the researchers was to test the chosen sample and come back to the system for further analysis and optimization of the findings. According to Mike Foshey, the Project Manager from MIT:
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.
As a prime example that AI is gradually turning from a buzzword to secure seed financing to a viable tool for spearheading innovation, the researchers have also decided to open-source the 3D printing algorithm. It can be found at the free AutoOED platform for materials innovation with all the accompanying research. The complete AuroOED software package lets those who are curious enough to experiment and optimize it further to suit their own needs, in a true open-source spirit.
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