Atomic engineering Featured

Machine learning coupled with mechanical strain and radiation nudging of atoms promise powerful new control over materials.
Ju Li Materials Day 2452 DP Web
MIT Professor Ju Li speaks at the MIT MRL Materials Day Symposium Oct. 9, 2019. Image, Denis Paiste, Materials Research Laboratory.

Exerting human will on materials atom-by-atom is an engineer’s dream. “Machine learning will help us get there,” Professor Ju Li, Battelle Energy Alliance Professor of Nuclear Science and Engineering, and Professor of Materials Science and Engineering, says. Today, his work to gain insights into improving materials is advantaged by machine learning and automated experimentation, tools that he suggests will take over much of the work of current day engineers. Li spoke at the MIT MRL Materials Day Symposium Oct. 9, 2019.

Li and colleagues first applied machine learning tools to control elastic strain, which is a combination of stretch, shear and pressure that allows a material to deform reversibly, focusing on nanoscale-sized silicon and diamond. These changes can affect the electronic and optical properties of materials, for example, strained silicon technology uses biaxial elastic strain of about 1 percent to boost electronic current flow by more than 50 percent. “With improvement in sample quality, you can stretch silicon nowadays by more than 10 percent, deeply into what I call the ultra-strength ocean,” Li says.

Research on niobium nanowires drawn inside a nickel-titanium shape memory alloy showed when this material is strained under a load, this material can undergo 6% elastic deformation reversibly up to half a million times, Li relates. Synchroton images show this is truly elastic deformation and not a phase change, he says.

“Niobium is a standard superconductor and with strain you can change its superconducting temperature,” Li says. Elastic strain in these nanowires can change the critical temperature by about 20 percent. “From an engineering point of view, if you have to use liquid helium cooling, this gives you a lot of advantage. And you can also change the critical magnetic field significantly, both the upper critical field and lower critical field.”

“With nanotechnology, we suddenly have a big explosion of samples which can sustain a large dynamic range of elastic strain in tension, in shear, in combined loading,” Li notes. But calculating these effects on materials needs to account for six different strain possibilities, making it an ideal problem to address with machine learning. “Because strain has six components that you can change, it’s difficult to visualize,” he says. This work can reveal, for example, the least-energy pathway of changes in the bandgap of a material in response to mechanical actuation.

Using machine learning and a neural network, Li was able to predict a strain pathway to turn silicon’s electronic properties from semiconducting to metallic. “You really need to be able to go far out in strain energy to be able to hit direct bandgap silicon, but if you hit that, you can make lasers and integrated photonics much more miniaturized, because you don’t need phonons to give off photons.” Li says.

The neural network also identified topological transitions of band structure at certain elastic strains, which allows the researchers to label and visualize them. These new techniques have a wide range of applications from topological quantum computing to solar cell technology. Li’s team applied them to diamond and, he says, “This is the first time we are able to get unobstructed visualization of the six-dimensional ideal strain surface of silicon and diamond.”

In earlier work, experimentalists have demonstrated that they could bend and stretch ultrafine needles of diamond without breaking it. “We can make diamond a direct bandgap material with much less energy than you need from experiment, and we can make the bandgap of diamond become that of gallium nitride,” he says.

“In collaboration with Ming Dao and Subra Suresh, we’re looking at this experiment again and we are basically finding that by judiciously choosing the direction of bending, you can make diamond to have exceedingly small bandgaps. So that has yet to be experimentally validated,” Li adds.

Another line of research focuses on guiding the well-collimated electron-beam in a transmission electron microscope to move individual atoms, such as an individual phosphorus atom on graphene. Experiments show the ability to rotate a carbon-phosphorus bond 180 degrees because of the momentum transfer from the relativistic electron to the phosphorus atom, Li says. “By changing the direction of the electron beam, we can also have the carbon-phosphorus bond rotate 90 degrees, and control clockwise versus counter-clockwise rotation. This flips the concept of radiation damage, because instead of damaging the sample, we can create precisely any atomic structure that we want.”

Using an analogy of moving a phosphorus atom to dribbling a soccer ball around a field, Li, explains, “Sometimes we actually kick out the phosphorus atom, and in that case we have to go back and pick out a new ball from the side of the field and then play again. So there is a tradeoff of throughput of atomic manipulation by electron radiation and the risk of losing the soccer ball, and we can play that game by using machine learning.”

Li continues, “The machine can learn and improve the theoretical calculations, so in the future we have this optimized balance between throughput, which we believe eventually can hit one millisecond per atom step, in other words, you can assuredly move 1,000 atomic steps in one second. And with that, we’ll be able to have what I call atomic engineering. This is the ability to exert human will onto single individual atoms, to dictate their precise location, spin, redox state, etc.”

back to newsletter Denis Paiste, Materials Research Laboratory
October 29, 2019 

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