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Accelerating the discovery of new materials for 3D printing

#artificialintelligence

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 MIT have developed a data-driven process that uses machine learning to optimize new 3D printing materials with multiple characteristics, like toughness and compression strength. By streamlining materials development, the system lowers costs and lessens the environmental impact by reducing the amount of chemical waste. The machine learning algorithm could also spur innovation by suggesting unique chemical formulations that human intuition might miss. "Materials development is still very much a manual process. A chemist goes into a lab, mixes ingredients by hand, makes samples, tests them, and comes to a final formulation. But rather than having a chemist who can only do a couple of iterations over a span of days, our system can do hundreds of iterations over the same time span," says Mike Foshey, a mechanical engineer and project manager in the Computational Design and Fabrication Group (CDFG) of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and co-lead author of the paper.


Machine-learning system accelerates discovery of new materials for 3D printing

#artificialintelligence

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 MIT have developed a data-driven process that uses machine learning to optimize new 3D printing materials with multiple characteristics, like toughness and compression strength. By streamlining materials development, the system lowers costs and lessens the environmental impact by reducing the amount of chemical waste. The machine learning algorithm could also spur innovation by suggesting unique chemical formulations that human intuition might miss. "Materials development is still very much a manual process. A chemist goes into a lab, mixes ingredients by hand, makes samples, tests them, and comes to a final formulation. But rather than having a chemist who can only do a couple of iterations over a span of days, our system can do hundreds of iterations over the same time span," says Mike Foshey, a mechanical engineer and project manager in the Computational Design and Fabrication Group (CDFG) of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and co-lead author of the paper.


Algorithms and Autonomous Discovery

IEEE Spectrum

More than a decade ago, Ichiro Takeuchi, professor of materials science and engineering, started applying the subfield of artificial intelligence (AI) known as machine learning (ML) to help develop new magnetic materials. At the time, ML was not widely used in materials science. "Now, it's all the rage," says Takeuchi, who also holds an appointment with the Maryland Energy Innovation Institute. Its current popularity is due in part to the deep learning revolution of 2012 and related advances in computer chip speed, data storage options, and rapid refinement of the science that drives its predictive analytics of algorithms. ML-based discovery in materials science is not just a lab exercise.


Towards a Content-Based Material Science Discovery Network

AAAI Conferences

Many publicly available databases exist for managing materials scientific data. These databases contain in- formation from a wide variety of work, and the infor- mation is typically encoded in some proprietary format aimed at highlighting the goals of their particular back- grounds and purposes. In order to accelerate the rate at which new materials are discovered, these databases must be federated to provide materials scientists with the means to efficiently access large quantities of highly relevant data. This position paper advocates the design of a content-based material science discovery network that can allow for more intelligent reasoning over the databases than current implementations can afford. We will discuss the gains of using a hierarchical ontology for describing metadata that captures the various layers of the materials science domain. We will then discuss our approach in a content-based networking context.


Can artificial intelligence open new doors for materials discovery?

#artificialintelligence

Looking for a shortcut, he found that neural networks, a type of artificial intelligence (AI) that uncovers patterns in huge data sets, can accurately predict …