UMD-NIST Self-Directing AI System Discovers New Material

When the words “artificial intelligence” (AI) come to mind, your first thoughts may be supercomputers, or robots that perform tasks without assistance from humans. Now, a multi-institutional team led by researchers from the University of Maryland (UMD) and National Institute of Standards and Technology (NIST) working with collaborators at Stanford University, University of Washington, University of Pennsylvania, and Duke University have accomplished something not too far off. They developed an AI algorithm called CAMEO that discovered a useful new material without requiring additional training from scientists. The AI system could help reduce the amount of trial-and-error time scientists spend in the lab, while maximizing productivity and efficiency in their research.

The research team published their work on CAMEO in Nature Communications on November 24, 2020.

In the field of materials science, scientists seek to discover new materials that can be used in specific applications. “For example, we are always looking are new quantum materials which can be used for quantum computers and sensors,” said physics affiliate Ichiro Takeuchi, a professor of materials science and engineering and member of the Quantum Materials Center (QMC) at UMD.

But finding such new materials usually takes a large number of coordinated experiments and time-consuming theoretical searches. If a researcher is interested in how a material’s properties vary with different temperatures, then that may mean 10 experiments at 10 different temperatures. Temperature, however, is just one parameter. If there are five parameters, each with 10 values, then that researcher must run the experiment 10 x 10 x 10 x 10 x 10 times, a total of 100,000 experiments. It’s nearly impossible for a researcher to run that many experiments via brute force due to the years or decades it may take.

That’s where CAMEO comes in. Short for Closed-Loop Autonomous System for Materials Exploration and Optimization, CAMEO can ensure that each experiment maximizes the scientist’s knowledge and understanding, skipping over experiments that would give redundant information. Helping scientists reach their goals faster with fewer experiments also enables  labs to use their limited resources more efficiently. But how is CAMEO able to do this?

Method Behind the Process

Active learning is a machine learning technique in which computer programs can access data and process it themselves, automatically updating the knowledge and deciding the optimum sequence of information acquisition. This is the basis for CAMEO, a self-learning AI that uses prediction and uncertainty to determine which experiment to try next.

As implied by its name, CAMEO looks for a useful new material by operating in a closed loop: it determines which experiment to run on a material, does the experiment, and collects the data. It can also ask for more information, such as the crystal structure of the desired material, from the scientist before running the next experiment, which is informed by all past experiments achieved in the loop.  

“The key to our experiment was that we were able to unleash CAMEO on a combinatorial library where we had made a large array of materials with all different compositions,” said Takeuchi. “In a usual combinatorial study, every material in the array would have been measured one by one to look for the compound with the best properties. Depending on the property of interest, even with a fast measurement setup, that can take a long, long time. With CAMEO, it only took a small fraction of total number of measurements to home in on the best material.”

The AI is also designed to contain knowledge of key principles, some of which includes knowledge of past simulations and lab experiments, how the equipment works, and physical concepts. For example, the researchers armed CAMEO with the knowledge of phase diagrams, which describes how the arrangement of atoms in a material changes with chemical composition and temperature.

Understanding how atoms are arranged in a material is important in determining its properties such as how hard, or how electrically-insulating it is, and how well it is suited for a specific application.

“The AI is unsupervised,” said NIST researcher, Aaron Gilad Kusne. “Many types of AI need to be trained or supervised. Instead of asking it to learn physical laws, we encode them into the AI. You don’t need a human to train the AI.”

One of the best ways to figure out the structure of a material is by bombarding it with x-rays, in a technique called x-ray diffraction. By identifying the angles at which the x-rays bounce off, scientists determine how atoms are arranged in a material, enabling them to figure out its crystal structure. However, a single in-house x-ray diffraction experiment can take an hour or more. At a synchrotron facility, a large machine the size of a football field that accelerates electrically charged particles at close to the speed of light, this process can take 10 seconds, because the fast-moving particles emit large numbers of x-rays. This is the method used in the study at the Stanford Synchrotron Radiation Lightsource.

CAMEO then decides which material composition to study next and focuses the x-rays on the appropriate part of the sample where that composition exists, to investigate its atomic structure. With each new iteration, CAMEO learns from past measurements and identifies the next material to study.  This allows the AI to explore how a material’s composition impacts its structure and use this information to find the best material for the task.

“Think of this process as trying to make the perfect cake,” Kusne said. “You’re mixing different types of ingredients, flour, eggs, or butter, using a variety of recipes to make the best cake. With the AI, it’s searching through the ‘recipes’ or experiments to determine the best composition for the material.”

That is how CAMEO discovered the material  which the group shortened to GST467. CAMEO was provided with 177 potential materials to investigate, covering a large range of compositional recipes. To arrive at this material, CAMEO performed 19 different experimental cycles, which took 10 hours, compared to the estimated 90 hours it would have taken a scientist with the full set of 177 materials.

The New Material

The material is composed of three different elements (germanium, antimony and tellurium, Ge-Sb-Te) and is a phase-change memory material, that is, it changes its atomic structure from crystalline (solid material with atoms in designated, regular positions) to amorphous (solid material with atoms in random positions) when quickly melted by applying heat. This type of material is used in memory applications such as data storage. Although there are infinite composition variations possible in the Ge-Sb-Te alloy system, the new material GST467 discovered by CAMEO is optimal for phase-change applications.

The research team wanted CAMEO to find the best Ge-Sb-Te alloy, one that had the largest difference in optical contrast between the crystalline and amorphous states. Optical contrast, for example on a DVD disc, allows a scanning laser to read the disc by distinguishing between regions that have high or low reflectivity. They found that GST467 has twice the contrast for a phase change material compared to GST225 or , a well-known material that’s commonly used for DVDs. The larger contrast enables the new material to outperform the old material by a significant margin.

The key part of the experiments was conducted at the Stanford National Accelerator Laboratory (SLAC) at Stanford University, for the U.S. Department of Energy Office of Science. SLAC researchers helped oversee the experiments run by CAMEO.

UMD researchers provided the materials used in the experiments and researchers at the University of Washington – led by Electrical and Computer Engineering Professor, Mo Li – demonstrated the new material in a patterned phase-change memory device. 

The new material GST467 has applications for photonic switching devices, which control the direction of light in a circuit. They can also be applied in neuromorphic computing, a field of study focused on developing devices that emulate the structure and function of neurons in the brain, opening possibilities for new kinds of computers as well as other applications such as extracting useful data from complex images.

The work also involved collaboration with electron microscopists at NIST who performed high-resolution microscopy to understand the microstructure of the newly found compound.

Applications to other Materials

The researchers believe CAMEO can be used for other types of materials, such as high-temperature alloys and quantum materials. The code for CAMEO is open source and will be freely available for use by scientists and researchers.

There had been other reports of closed-loop materials and chemistry optimization work. The critical distinguishing feature of the present work with CAMEO is that it was used to discover a novel solid state material whose functionality is encoded in the composition-structure-property relationship of crystalline materials, and as such, the algorithm was able to navigate the course of discovery path by tracking the structural origins of materials functionalities.

One application of CAMEO is minimizing experimental costs since using synchrotron facilities requires time, researchers need a written proposal to use the equipment, and money. But with AI running the experiments, they can be carried out quicker. Researchers estimate a 10-fold reduction in time for experiments using CAMEO since the number of experiments performed can be cut by one tenth. Because the AI is running the measurements, collecting data and performing the  analysis, this also reduces the amount of knowledge a researcher needs to run the experiment. All the researcher must focus on is running the AI.

Another potential benefit is providing the ability to work remotely for scientists. “This opens up a wave of scientists to still work and be productive without actually being in the lab,” said SLAC researcher Apurva Mehta. This could mean if scientists wanted to work on research involving contagious diseases or viruses, such as COVID-19, they could do so safely and remotely while relying on the AI to conduct the experiments in the lab.

Researchers are continuing to improve the AI and try to make the algorithms capable of solving ever more complex problems. “The ultimate goal is to incorporate synthesis of crystalline materials in the closed loop – this is particularly hard since standard synthesis tools of crystalline functional materials are not equipped with measurement capabilities,” said Takeuchi. “That calls for some novel hardware integration as well as advances in AI. The future is robot materials science.”  

Original story:

Das Sarma, Monroe Named 2020 Highly Cited Researchers

Sankar Das Sarma and Chris Monroe are included on the Clarivate Web of Science Group’s 2020 roster of Highly Cited Researchers(link is external) r, which recognizes influential scientists for their highly cited papers over the preceding decade. Both are Distinguished University Professors and Fellows of the Joint Quantum InstituteClarivate Highly Cited

Das Sarma is Director of the Condensed Matter Theory Center and holds the Richard E. Prange Chair. Monroe holds the Bice Zorn Professorship and is a Fellow of the Joint Center for Quantum Information and Computer Science.

Das Sarma has been included every year that the list has been released. This is Monroe’s second consecutive year receiving the distinction.

Das Sarma explores the theories behind condensed matter physics, statistical mechanics and quantum information, while Monroe performs experiments related to atomic physics and quantum information science. Both researchers have contributed new ideas that pushed the boundaries of the burgeoning field of quantum computing.

Original story by Bailey Bedford:



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Going Beyond the Anti-Laser May Enable Long-Range Wireless Power Transfer

Ever since Nikola Tesla spewed electricity out in all directions with his coil back in 1891, scientists have been thinking up ways to send electrical power through the air. The dream is to charge your phone or laptop, or maybe even a healthcare device such as a pacemaker, without the need for wires and plugs. The tricky bit is getting the electricity to find its intended target, and getting that target to absorb the electricity instead of just reflect it back into the air—all preferably without endangering anyone along the way.

These days, you can wirelessly charge a smartphone by putting it within an inch of a charging station. But usable long-range wireless power transfer, from one side of a room to another or even across a building, is still a work in progress. Most of the methods currently in development involve focusing narrow beams of energy and aiming them at their intended target. These methods have had some success, but are so far not very efficient. And having focused electromagnetic beams flying around through the air is unsettling.Arcs of electricity generated by a Tesla coil. (Credit: Airarcs/CC BY-SA 3.0)Arcs of electricity generated by a Tesla coil. (Credit: Airarcs/CC BY-SA 3.0)

Now, a team of researchers at the University of Maryland (UMD), in collaboration with a colleague at Wesleyan University in Connecticut, have developed an improved technique for wireless power transfer technology that may promise long-range power transmission without narrowly focused and directed energy beams. Their results, which widen the applicability of previous techniques, were published Nov. 17, 2020 in the journal Nature Communications.

The team generalized a concept known as an “anti-laser.” In a laser, one photon triggers a cascade of many photons of the same color shooting out in a coherent beam. In an anti-laser, the reverse happens. Instead of boosting the number of photons, an anti-laser coherently and perfectly absorbs a beam of many precisely tuned photons. It’s kind of like a laser running backwards in time.

The new work, led by UMD Professor of Physics Steven Anlage of the Quantum Materials Center (QMC), demonstrates that it’s possible to design a coherent perfect absorber outside of the original time-reversed laser framework—a relaxation of some of the key constraints in earlier work. Instead of assuming directed beams traveling along straight lines into an absorption target, they picked a geometry that was disorderly and not amenable to being run backwards in time.

“We wanted to see this effect in a completely general environment where there's no constraints,” says Anlage. “We wanted a sort of random, arbitrary, complex environment, and we wanted to make perfect absorption happen under those really demanding circumstances. That was the motivation for this, and we did it.”

Anlage and his colleagues wanted to create a device that could receive energy from a more diffuse source, something that was less beam and more bath. Before tackling the wireless challenge, they set up their generalized anti-laser as a labyrinth of wires for electromagnetic waves to travel through. Specifically, they used microwaves, a common candidate for power transfer applications. The labyrinth consisted of a bunch of wires and boxes connected in a purposefully disordered way. Microwaves going through this labyrinth would get so tangled up that, even if it were possible to reverse time, this still wouldn’t untangle them.

Buried in the midst of this labyrinth was an absorber, the target to deliver power to. The team sent microwaves of different frequencies, amplitudes and phases into the labyrinth and measured how they were transformed. Based on these measurements, they were able to calculate the exact properties of input microwaves that would result in perfect power transfer to the absorber. They found that for correctly chosen input microwaves, the labyrinth absorbed an unprecedented 99.999% of the power they sent into it. This showed explicitly that coherent perfect absorption can be achieved even without a laser run backwards in time.

The team then took a step towards wireless power transfer. They repeated the experiment in a cavity, a plate of brass several feet in each direction with an oddly shaped hole in the middle. The shape of the hole was designed so that the microwaves would bounce around it in an unpredictable, chaotic way. They placed a power absorber inside the cavity, and sent microwaves in to bounce around the open space inside. They were able to find the right input microwave conditions for coherent perfect absorption with 99.996% efficiency.

Recent work by a collaboration of teams in France and Austria also demonstrated coherent perfect absorption in their own disordered microwave labyrinth. However, their experiment was not quite as general as the new work from Anlage and colleagues. In the previous work, the microwaves entering the labyrinth would still be untangled by a hypothetical reversal of time. This might seem like a subtle distinction, but the authors say showing that coherent perfect absorption doesn’t require any kind of order in the environment promises applicability virtually anywhere.

Generalizing previous techniques in this way invites ideas that sound like science fiction, like being able to wirelessly and remotely charge any object in a complex environment, such as an office building, with near perfect efficiency. Such schemes would require that the frequency, amplitude, and phase of the electric power is custom tuned to specific targets. But there would be no need to focus a high-powered beam and aim it at the laptop or phone—the electrical waves themselves would be designed to find their chosen target.

“If we have an object which we want to deliver power to, we will first use our equipment to measure some properties of the system,” says Lei Chen, a graduate student in electrical and computer engineering at UMD and the lead author of the paper. “Based on those properties we can get the unique microwave signals for this kind of system. And it will be perfectly absorbed by the object. For every unique object, the signals will be different and specially designed.”

Although this technique shows great promise, much remains to be done before the advent of wireless and plug-less offices. The perfect absorber depends crucially on the power being tuned just right for the absorber. A slight change in the environment—such as moving the target laptop or raising the blinds in the room—would require an immediate retuning of all the parameters. So, there would need to be a way to quickly and efficiently find the right conditions for perfect absorption on the fly, without using too much power or bandwidth. Additionally, more work needs to be done to determine the efficacy and safety of this technique in realistic environments.

Even though it’s not yet time to throw away all your power cords, coherent perfect absorption may come in handy in many ways. Not only is it general to any kind of target, it is also not limited to optics or microwaves.  “It's not wedded to one specific technology,” says Anlage, “This is a very general wave phenomenon. And the fact that it's done in microwaves is just because that's where the strengths are in my lab. But you could do all of this with acoustics, you could do this with matter waves, you could do this with cold atoms. You could do this in many, many different contexts.”

In addition to Chen and Anlage, Tsampikos Kottos, a professor at Wesleyan University, was a co-author on the paper.

Written by Dina Genkina (This email address is being protected from spambots. You need JavaScript enabled to view it.)


PRB Highlights Work of Das Sarma and Hwang

To mark the 50th anniversary of Physical Review B, editors selected “milestone” papers that have made lasting contributions to condensed matter physics, including one co-written by Distinguished University Professor Sankar Das Sarma.pr50 social cropped ratio 0

Das Sarma wrote the selected paper, Dielectric function, screening, and plasmons in two-dimensional graphene, with Euyheon Hwang. Hwang earned his doctorate in 1996 under Das Sarma, and after appointments as a UMD research associate and assistant research scientist, accepted a faculty post at Sungkyunkwan University (SKKU) in South Korea.  He is one of about 100 of Das Sarma’s students and postdocs who have gone on to faculty appointHwang DasSarma 2003Euyheon Hwang (seated, yellow shirt) and Sankar Das Sarma (red shirt) with CMTC colleagues in 2003.ments.

Hwang and Das Sarma have written about 120 articles together, including 88 papers in PRB from 1994 to 2019.

The milestone paper was published in 2007 and has 1,744 citations. In it, the authors developed a many body theory for the dynamical dielectric function of doped graphene at an arbitrary wave vector and frequency.   The dielectric function directly determines many physical properties, including electrical and optical properties.  This ‘milestone’ publication by Hwang and Das Sarma has been instrumental not only in the development of the fundamental physics of graphene, but has also ushered in the technological field of ‘graphene plasmonics’ which is being widely pursued worldwide for practical engineering use in optics and photonics.

Das Sarma, the Richard E. Prange Chair in Physics, is a Distinguished University Professor, a Fellow of the Joint Quantum Institute, and the director of the Condensed Matter Theory Center. He is internationally known for his work on topological quantum computation, Majorana physics, spin quantum computation, many body phenomena, quantum localization and nonequlibrium statistical mechanics, and has recently entered into the study of twisted bilayer graphene and higher-order topological systems. Google Scholar counts 90,227 citations and calculates an h-index of 124.



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