Machine Learning in Materials Research Featured

Annual MIT Materials Day Symposium highlights latest innovations on Oct. 9, 2019.

Machine learning tools are both helping to design new materials and devices and to help those devices run at their best.

MIT Associate Professor of Materials Science and Engineering Juejun (JJ) Hu

Optical spectrometers, for example, are devices that record Iight intensity as a function of wavelength and identify chemicals based on their response to light. MIT Associate Professor of Materials Science and Engineering Juejun (JJ) Hu, last year developed a new chip-based spectrometer that employs an algorithm which improves resolution 100 percent compared to the textbook limits, called Rayleigh limits.

“We developed an algorithm that allows us to extract the information with much better signal-to-noise ratio,” Hu explains. “We have validated the algorithm for many different kinds of spectrum.”

Unlike the conventional shape of glass lenses which are often curved, his new optical devices feature an array of specially designed optical antennas that add a phase delay to the incoming light, which enables many different functions. Hu currently is working with UMass researchers to perfect an algorithm that can screen potential designs for these devices. The algorithm can evaluate the workability of irregular shapes that go beyond conventional shapes likes circles and rectangles.

“The algorithm allows us to train it with existing data,” Hu says. “It can recognize the underlying connections between complex geometries and the electromagnetic response.” The algorithm can find hidden relations much faster than conventional full-scale simulation methods. The algorithm can also screen out potential combinations of materials and functions that just won’t work. “If you use conventional methods, you have to waste lots of time to exhaust all the possible design space and then come to this conclusion, but now our algorithm can tell you really quickly,” he says.

Hu will present his research at the MIT Materials Research Laboratory’s annual Materials Day Symposium on Wednesday, Oct. 9, in Kresge Auditorium. The Symposium runs from 8 a.m. to 3:30 p.m. and is immediately followed by a Poster Session in La Sala de Puerto Rico on the second floor of Stratton Student Center. Register here.

MIT Atlantic Richfield Associate Professor of Energy Studies Elsa A. Olivetti

Atlantic Richfield Associate Professor of Energy Studies Elsa A. Olivetti will discuss her work on an artificial-intelligence system that scours through scientific papers to deduce materials science “recipes.” Her team is currently working on experimental verification, particularly focused on catalysts materials.

“We are constantly refining and improving our system from improving overall accuracy to expanding to other parts of the paper, such as results, to other kinds of documents, such as patents,” Olivetti says.

AI can also help to improve sustainability. “If we can know better how to make new materials, we might be able to inform how to make them in a lower resource consuming way,” Olivetti says.

Keynote speaker Dr. Brian Storey, Toyota Research Institute’s Director of Accelerated Materials Design & Discovery, will discuss several collaborative projects focusing on research and development of materials for battery and fuel cell electric vehicles.

Other Materials Day speakers are: Professor Carl V. Thompson, Director, Materials Research Laboratory; Professor Klavs F. Jensen, Departments of Chemical Engineering and Materials Science & Engineering; Professor Asu Ozdaglar, Department Head, Electrical Engineering & Computer Science; Professor Ju Li, Departments of Nuclear Science & Engineering and Materials Science & Engineering; and Assistant Professor Rafael Gomez-Bombarelli, Department of Materials Science & Engineering.

MIT graduate students and postdocs will give two-minute talks on their research during a “Poster Previews” session before the lunch break. The Poster Session runs 3:35 to 5:45 p.m. with an awards presentation at 5:30 p.m.

back to newsletterDenis Paiste, Materials Research Laboratory
September 25, 2019

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