Design on a Diamond: AI's Potential in Advanced Materials Research

#artificialintelligence

Applying just a bit of strain to a piece of semiconductor or other crystalline material can deform the orderly arrangement of atoms in its structure enough to cause dramatic changes in its properties, such as the way it conducts electricity, transmits light, or conducts heat. Now, a team of researchers at MIT and in Russia and Singapore have found ways to use artificial intelligence to help predict and control these changes, potentially opening up new avenues of research on advanced materials for future high-tech devices. The findings appeared in early February in the Proceedings of the National Academy of Sciences, in a paper authored by MIT professor of nuclear science and engineering and of materials science and engineering Ju Li, MIT Principal Research Scientist Ming Dao, and MIT graduate student Zhe Shi, with Evgenii Tsymbalov and Alexander Shapeev at the Skolkovo Institute of Science and Technology in Russia, and Subra Suresh, the Vannevar Bush Professor Emeritus and former dean of engineering at MIT and current president of Nanyang Technological University in Singapore. Already, based on earlier work at MIT, some degree of elastic strain has been incorporated in some silicon processor chips. Even a 1% change in the structure can in some cases improve the speed of the device by 50 percent, by allowing electrons to move through the material faster.


Giant polarization in super-tetragonal thin films through interphase strain

Science

Strain engineering has emerged as a powerful tool to enhance the performance of known functional materials. Here we demonstrate a general and practical method to obtain super-tetragonality and giant polarization using interphase strain. We use this method to create an out-of-plane–to–in-plane lattice parameter ratio of 1.238 in epitaxial composite thin films of tetragonal lead titanate (PbTiO3), compared to 1.065 in bulk. These thin films with super-tetragonal structure possess a giant remanent polarization, 236.3 microcoulombs per square centimeter, which is almost twice the value of known ferroelectrics. The super-tetragonal phase is stable up to 725 C, compared to the bulk transition temperature of 490 C. The interphase-strain approach could enhance the physical properties of other functional materials.


[Report] Direct and continuous strain control of catalysts with tunable battery electrode materials

Science

We report a method for using battery electrode materials to directly and continuously control the lattice strain of platinum (Pt) catalyst and thus tune its catalytic activity for the oxygen reduction reaction (ORR). Whereas the common approach of using metal overlayers introduces ligand effects in addition to strain, by electrochemically switching between the charging and discharging status of battery electrodes the change in volume can be precisely controlled to induce either compressive or tensile strain on supported catalysts. Lattice compression and tension induced by the lithium cobalt oxide substrate of 5% were directly observed in individual Pt nanoparticles with aberration-corrected transmission electron microscopy. We observed 90% enhancement or 40% suppression in Pt ORR activity under compression or tension, respectively, which is consistent with theoretical predictions.


Introducing the 2018 Summer Scholars

MIT News

For Fernando Nieves Munoz, a mechanical engineering major at the University of Puerto Rico at Mayaguez, coming to MIT has been a dream since a very young age. This summer, he'll get to fulfill that ambition as one of 12 top-ranked undergraduate Summer Scholars selected by the MIT Materials Research Laboratory to conduct graduate-level research. "As an engineering student, I expect to grow and be deeply immersed as an educator and researcher in the future," Nieves says. He hopes to join a project to develop and enhance properties of new materials by analyzing their behaviors and structures. Interest in different aspects of renewable energy is the most popular research theme among this year's student interns.


Using artificial intelligence to engineer materials' properties

#artificialintelligence

Applying just a bit of strain to a piece of semiconductor or other crystalline material can deform the orderly arrangement of atoms in its structure enough to cause dramatic changes in its properties, such as the way it conducts electricity, transmits light, or conducts heat. Now, a team of researchers at MIT and in Russia and Singapore have found ways to use artificial intelligence to help predict and control these changes, potentially opening up new avenues of research on advanced materials for future high-tech devices. The findings appear this week in the Proceedings of the National Academy of Sciences, in a paper authored by MIT professor of nuclear science and engineering and of materials science and engineering Ju Li, MIT Principal Research Scientist Ming Dao, and MIT graduate student Zhe Shi, with Evgeni Tsymbalov and Alexander Shapeev at the Skolkovo Institute of Science and Technology in Russia, and Subra Suresh, the Vannevar Bush Professor Emeritus and former dean of engineering at MIT and current president of Nanyang Technological University in Singapore. Already, based on earlier work at MIT, some degree of elastic strain has been incorporated in some silicon processor chips. Even a 1 percent change in the structure can in some cases improve the speed of the device by 50 percent, by allowing electrons to move through the material faster.