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    3 Questions: What’s it like winning the MIT $100K Entrepreneurship Competition?

    Solar power plays a major role in nearly every roadmap for global decarbonization. But solar panels are large, heavy, and expensive, which limits their deployment. But what if solar panels looked more like a yoga mat?

    Such a technology could be transported in a roll, carried to the top of a building, and rolled out across the roof in a matter of minutes, slashing installation costs and dramatically expanding the places where rooftop solar makes sense.

    That was the vision laid out by the MIT spinout Active Surfaces as part of the winning pitch at this year’s MIT $100K Entrepreneurship Competition, which took place May 15. The company is leveraging materials science and manufacturing innovations from labs across MIT to make ultra-thin, lightweight, and durable solar a reality.

    The $100K is one of MIT’s most visible entrepreneurship competitions, and past winners say the prize money is only part of the benefit that winning brings to a burgeoning new company. MIT News sat down with Active Surface founders Shiv Bhakta, a graduate student in MIT’s Leaders for Global Operations dual-degree program within the MIT Sloan School of Management and Department of Civil and Environmental Engineering, and Richard Swartwout SM ’18 PhD ’21, an electrical engineering and computer science graduate and former Research Laboratory of Electronics postdoc and MIT.nano innovation fellow, to learn what the last couple of months have been like since they won.

    Q: What is Active Surfaces’ solution, and what is its potential?

    Bhakta: We’re commercializing an ultrathin film, flexible solar technology. Solar is one of the most broadly distributed resources in the world, but access is limited today. It’s heavy — it weighs 50 to 60 pounds a panel — it requires large teams to move around, and the form factor can only be deployed in specific environments.

    Our approach is to develop a solar technology for the built environment. In a nutshell, we can create flexible solar panels that are as thin as paper, just as efficient as traditional panels, and at unprecedented cost floors, all while being applied to any surface. Same area, same power. That’s our motto.

    When I came to MIT, my north star was to dive deeper in my climate journey and help make the world a better, greener place. Now, as we build Active Surfaces, I’m excited to see that dream taking shape. The prospect of transforming any surface into an energy source, thereby expanding solar accessibility globally, holds the promise of significantly reducing CO2 emissions at a gigaton scale. That’s what gets me out of bed in the morning.

    Swartwout: Solar and a lot of other renewables tend to be pretty land-inefficient. Solar 1.0 is using low hanging fruit: cheap land next to easy interconnects and new buildings designed to handle the weight of current panels. But as we ramp up solar, those things will run out. We need to utilize spaces and assets better. That’s what I think solar 2.0 will be: urban PV deployments, solar that’s closer to demand, and integrated into the built environment. These next-generation use cases aren’t just a racking system in the middle of nowhere.

    We’re going after commercial roofs, which would cover most [building] energy demand. Something like 80-90 percent of building electricity demands in the space can be met by rooftop solar.

    The goal is to do the manufacturing in-house. We use roll-to-roll manufacturing, so we can buy tons of equipment off the shelf, but most roll-to-roll manufacturing is made for things like labeling and tape, and not a semiconductor, so our plan is to be the core of semiconductor roll-to-roll manufacturing. There’s never been roll-to-roll semiconductor manufacturing before.

    Q: What have the last few months been like since you won the $100K competition?

    Bhakta: After winning the $100K, we’ve gotten a lot of inbound contact from MIT alumni. I think that’s my favorite part about the MIT community — people stay connected. They’ve been congratulating us, asking to chat, looking to partner, deploy, and invest.

    We’ve also gotten contacted by previous $100K competition winners and other startups that have spun out of MIT that are a year or two or three ahead of us in terms of development. There are a lot of startup scaling challenges that other startup founders are best equipped to answer, and it’s been huge to get guidance from them.

    We’ve also gotten into top accelerators like Cleantech Open, Venture For Climatetech, and ACCEL at Greentown Labs. We also onboarded two rockstar MIT Sloan interns for the summer. Now we’re getting to the product-development phase, building relationships with potential pilot partners, and scaling up the area of our technology.      

    Swartwout: Winning the $100K competition was a great point of validation for the company, because the judges themselves are well known in the venture capital community as well as people who have been in the startup ecosystem for a long time, so that has really propelled us forward. Ideally, we’ll be getting more MIT alumni to join us to fulfill this mission.

    Q: What are your plans for the next year or so?

    Swartwout: We’re planning on leveraging open-access facilities like those at MIT.nano and the University of Massachusetts Amherst. We’re pretty focused now on scaling size. Out of the lab, [the technology] is a 4-inch by 4-inch solar module, and the goal is to get up to something that’s relevant for the industry to offset electricity for building owners and generate electricity for the grid at a reasonable cost.

    Bhakta: In the next year, through those open-access facilities, the goal is to go from 100-millimeter width to 300-millimeter width and a very long length using a roll-to-roll manufacturing process. That means getting through the engineering challenges of scaling technology and fine tuning the performance.

    When we’re ready to deliver a pilotable product, it’s my job to have customers lined up ready to demonstrate this works on their buildings, sign longer term contracts to get early revenue, and have the support we need to demonstrate this at scale. That’s the goal. More

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    Chemists discover why photosynthetic light-harvesting is so efficient

    When photosynthetic cells absorb light from the sun, packets of energy called photons leap between a series of light-harvesting proteins until they reach the photosynthetic reaction center. There, cells convert the energy into electrons, which eventually power the production of sugar molecules.

    This transfer of energy through the light-harvesting complex occurs with extremely high efficiency: Nearly every photon of light absorbed generates an electron, a phenomenon known as near-unity quantum efficiency.

    A new study from MIT chemists offers a potential explanation for how proteins of the light-harvesting complex, also called the antenna, achieve that high efficiency. For the first time, the researchers were able to measure the energy transfer between light-harvesting proteins, allowing them to discover that the disorganized arrangement of these proteins boosts the efficiency of the energy transduction.

    “In order for that antenna to work, you need long-distance energy transduction. Our key finding is that the disordered organization of the light-harvesting proteins enhances the efficiency of that long-distance energy transduction,” says Gabriela Schlau-Cohen, an associate professor of chemistry at MIT and the senior author of the new study.

    MIT postdocs Dihao Wang and Dvir Harris and former MIT graduate student Olivia Fiebig PhD ’22 are the lead authors of the paper, which appears this week in the Proceedings of the National Academy of Sciences. Jianshu Cao, an MIT professor of chemistry, is also an author of the paper.

    Energy capture

    For this study, the MIT team focused on purple bacteria, which are often found in oxygen-poor aquatic environments and are commonly used as a model for studies of photosynthetic light-harvesting.

    Within these cells, captured photons travel through light-harvesting complexes consisting of proteins and light-absorbing pigments such as chlorophyll. Using ultrafast spectroscopy, a technique that uses extremely short laser pulses to study events that happen on timescales of femtoseconds to nanoseconds, scientists have been able to study how energy moves within a single one of these proteins. However, studying how energy travels between these proteins has proven much more challenging because it requires positioning multiple proteins in a controlled way.

    To create an experimental setup where they could measure how energy travels between two proteins, the MIT team designed synthetic nanoscale membranes with a composition similar to those of naturally occurring cell membranes. By controlling the size of these membranes, known as nanodiscs, they were able to control the distance between two proteins embedded within the discs.

    For this study, the researchers embedded two versions of the primary light-harvesting protein found in purple bacteria, known as LH2 and LH3, into their nanodiscs. LH2 is the protein that is present during normal light conditions, and LH3 is a variant that is usually expressed only during low light conditions.

    Using the cryo-electron microscope at the MIT.nano facility, the researchers could image their membrane-embedded proteins and show that they were positioned at distances similar to those seen in the native membrane. They were also able to measure the distances between the light-harvesting proteins, which were on the scale of 2.5 to 3 nanometers.

    Disordered is better

    Because LH2 and LH3 absorb slightly different wavelengths of light, it is possible to use ultrafast spectroscopy to observe the energy transfer between them. For proteins spaced closely together, the researchers found that it takes about 6 picoseconds for a photon of energy to travel between them. For proteins farther apart, the transfer takes up to 15 picoseconds.

    Faster travel translates to more efficient energy transfer, because the longer the journey takes, the more energy is lost during the transfer.

    “When a photon gets absorbed, you only have so long before that energy gets lost through unwanted processes such as nonradiative decay, so the faster it can get converted, the more efficient it will be,” Schlau-Cohen says.

    The researchers also found that proteins arranged in a lattice structure showed less efficient energy transfer than proteins that were arranged in randomly organized structures, as they usually are in living cells.

    “Ordered organization is actually less efficient than the disordered organization of biology, which we think is really interesting because biology tends to be disordered. This finding tells us that that may not just be an inevitable downside of biology, but organisms may have evolved to take advantage of it,” Schlau-Cohen says.

    Now that they have established the ability to measure inter-protein energy transfer, the researchers plan to explore energy transfer between other proteins, such as the transfer between proteins of the antenna to proteins of the reaction center. They also plan to study energy transfer between antenna proteins found in organisms other than purple bacteria, such as green plants.

    The research was funded primarily by the U.S. Department of Energy. More

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    Moving perovskite advancements from the lab to the manufacturing floor

    The following was issued as a joint announcement from MIT.nano and the MIT Research Laboratory for Electronics; CubicPV; Verde Technologies; Princeton University; and the University of California at San Diego.

    Tandem solar cells are made of stacked materials — such as silicon paired with perovskites — that together absorb more of the solar spectrum than single materials, resulting in a dramatic increase in efficiency. Their potential to generate significantly more power than conventional cells could make a meaningful difference in the race to combat climate change and the transition to a clean-energy future.

    However, current methods to create stable and efficient perovskite layers require time-consuming, painstaking rounds of design iteration and testing, inhibiting their development for commercial use. Today, the U.S. Department of Energy Solar Energy Technologies Office (SETO) announced that MIT has been selected to receive an $11.25 million cost-shared award to establish a new research center to address this challenge by using a co-optimization framework guided by machine learning and automation.

    A collaborative effort with lead industry participant CubicPV, solar startup Verde Technologies, and academic partners Princeton University and the University of California San Diego (UC San Diego), the center will bring together teams of researchers to support the creation of perovskite-silicon tandem solar modules that are co-designed for both stability and performance, with goals to significantly accelerate R&D and the transfer of these achievements into commercial environments.

    “Urgent challenges demand rapid action. This center will accelerate the development of tandem solar modules by bringing academia and industry into closer partnership,” says MIT professor of mechanical engineering Tonio Buonassisi, who will direct the center. “We’re grateful to the Department of Energy for supporting this powerful new model and excited to get to work.”

    Adam Lorenz, CTO of solar energy technology company CubicPV, stresses the importance of thinking about scale, alongside quality and efficiency, to accelerate the perovskite effort into the commercial environment. “Instead of chasing record efficiencies with tiny pixel-sized devices and later attempting to stabilize them, we will simultaneously target stability, reproducibility, and efficiency,” he says. “It’s a module-centric approach that creates a direct channel for R&D advancements into industry.”

    The center will be named Accelerated Co-Design of Durable, Reproducible, and Efficient Perovskite Tandems, or ADDEPT. The grant will be administered through the MIT Research Laboratory for Electronics (RLE).

    David Fenning, associate professor of nanoengineering at UC San Diego, has worked with Buonassisi on the idea of merging materials, automation, and computation, specifically in this field of artificial intelligence and solar, since 2014. Now, a central thrust of the ADDEPT project will be to deploy machine learning and robotic screening to optimize processing of perovskite-based solar materials for efficiency and durability.

    “We have already seen early indications of successful technology transfer between our UC San Diego robot PASCAL and industry,” says Fenning. “With this new center, we will bring research labs and the emerging perovskite industry together to improve reproducibility and reduce time to market.”

    “Our generation has an obligation to work collaboratively in the fight against climate change,” says Skylar Bagdon, CEO of Verde Technologies, which received the American-Made Perovskite Startup Prize. “Throughout the course of this center, Verde will do everything in our power to help this brilliant team transition lab-scale breakthroughs into the world where they can have an impact.”

    Several of the academic partners echoed the importance of the joint effort between academia and industry. Barry Rand, professor of electrical and computer engineering at the Andlinger Center for Energy and the Environment at Princeton University, pointed to the intersection of scientific knowledge and market awareness. “Understanding how chemistry affects films and interfaces will empower us to co-design for stability and performance,” he says. “The center will accelerate this use-inspired science, with close guidance from our end customers, the industry partners.”

    A critical resource for the center will be MIT.nano, a 200,000-square-foot research facility set in the heart of the campus. MIT.nano Director Vladimir Bulović, the Fariborz Maseeh (1990) Professor of Emerging Technology, says he envisions MIT.nano as a hub for industry and academic partners, facilitating technology development and transfer through shared lab space, open-access equipment, and streamlined intellectual property frameworks.

    “MIT has a history of groundbreaking innovation using perovskite materials for solar applications,” says Bulović. “We’re thrilled to help build on that history by anchoring ADDEPT at MIT.nano and working to help the nation advance the future of these promising materials.”

    MIT was selected as a part of the SETO Fiscal Year 2022 Photovoltaics (PV) funding program, an effort to reduce costs and supply chain vulnerabilities, further develop durable and recyclable solar technologies, and advance perovskite PV technologies toward commercialization. ADDEPT is one project that will tackle perovskite durability, which will extend module life. The overarching goal of these projects is to lower the levelized cost of electricity generated by PV.

    Research groups involved with the ADDEPT project at MIT include Buonassisi’s Accelerated Materials Laboratory for Sustainability (AMLS), Bulović’s Organic and Nanostructured Electronics (ONE) Lab, and the Bawendi Group led by Lester Wolfe Professor in Chemistry Moungi Bawendi. Also working on the project is Jeremiah Mwaura, research scientist in the ONE Lab. More

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    Exploring the nanoworld of biogenic gems

    A new research collaboration with The Bahrain Institute for Pearls and Gemstones (DANAT) will seek to develop advanced characterization tools for the analysis of the properties of pearls and to explore technologies to assign unique identifiers to individual pearls.

    The three-year project will be led by Admir Mašić, associate professor of civil and environmental engineering, in collaboration with Vladimir Bulović, the Fariborz Maseeh Chair in Emerging Technology and professor of electrical engineering and computer science.

    “Pearls are extremely complex and fascinating hierarchically ordered biological materials that are formed by a wide range of different species,” says Mašić. “Working with DANAT provides us a unique opportunity to apply our lab’s multi-scale materials characterization tools to identify potentially species-specific pearl fingerprints, while simultaneously addressing scientific research questions regarding the underlying biomineralization processes that could inform advances in sustainable building materials.”

    DANAT is a gemological laboratory specializing in the testing and study of natural pearls as a reflection of Bahrain’s pearling history and desire to protect and advance Bahrain’s pearling heritage. DANAT’s gemologists support clients and students through pearl, gemstone, and diamond identification services, as well as educational courses.

    Like many other precious gemstones, pearls have been human-made through scientific experimentation, says Noora Jamsheer, chief executive officer at DANAT. Over a century ago, cultured pearls entered markets as a competitive product to natural pearls, similar in appearance but different in value.

    “Gemological labs have been innovating scientific testing methods to differentiate between natural pearls and all other pearls that exist because of direct or indirect human intervention. Today the world knows natural pearls and cultured pearls. However, there are also pearls that fall in between these two categories,” says Jamsheer. “DANAT has the responsibility, as the leading gemological laboratory for pearl testing, to take the initiative necessary to ensure that testing methods keep pace with advances in the science of pearl cultivation.”

    Titled “Exploring the Nanoworld of Biogenic Gems,” the project will aim to improve the process of testing and identifying pearls by identifying morphological, micro-structural, optical, and chemical features sufficient to distinguish a pearl’s area of origin, method of growth, or both. MIT.nano, MIT’s open-access center for nanoscience and nanoengineering will be the organizational home for the project, where Mašić and his team will utilize the facility’s state-of-the-art characterization tools.

    In addition to discovering new methodologies for establishing a pearl’s origin, the project aims to utilize machine learning to automate pearl classification. Furthermore, researchers will investigate techniques to create a unique identifier associated with an individual pearl.

    The initial sponsored research project is expected to last three years, with potential for continued collaboration based on key findings or building upon the project’s success to open new avenues for research into the structure, properties, and growth of pearls. More

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    A simple way to significantly increase lifetimes of fuel cells and other devices

    In research that could jump-start work on a range of technologies including fuel cells, which are key to storing solar and wind energy, MIT researchers have found a relatively simple way to increase the lifetimes of these devices: changing the pH of the system.

    Fuel and electrolysis cells made of materials known as solid metal oxides are of interest for several reasons. For example, in the electrolysis mode, they are very efficient at converting electricity from a renewable source into a storable fuel like hydrogen or methane that can be used in the fuel cell mode to generate electricity when the sun isn’t shining or the wind isn’t blowing. They can also be made without using costly metals like platinum. However, their commercial viability has been hampered, in part, because they degrade over time. Metal atoms seeping from the interconnects used to construct banks of fuel/electrolysis cells slowly poison the devices.

    “What we’ve been able to demonstrate is that we can not only reverse that degradation, but actually enhance the performance above the initial value by controlling the acidity of the air-electrode interface,” says Harry L. Tuller, the R.P. Simmons Professor of Ceramics and Electronic Materials in MIT’s Department of Materials Science and Engineering (DMSE).

    The research, initially funded by the U.S. Department of Energy through the Office of Fossil Energy and Carbon Management’s (FECM) National Energy Technology Laboratory, should help the department meet its goal of significantly cutting the degradation rate of solid oxide fuel cells by 2035 to 2050.

    “Extending the lifetime of solid oxide fuels cells helps deliver the low-cost, high-efficiency hydrogen production and power generation needed for a clean energy future,” says Robert Schrecengost, acting director of FECM’s Division of Hydrogen with Carbon Management. “The department applauds these advancements to mature and ultimately commercialize these technologies so that we can provide clean and reliable energy for the American people.”

    “I’ve been working in this area my whole professional life, and what I’ve seen until now is mostly incremental improvements,” says Tuller, who was recently named a 2022 Materials Research Society Fellow for his career-long work in solid-state chemistry and electrochemistry. “People are normally satisfied with seeing improvements by factors of tens-of-percent. So, actually seeing much larger improvements and, as importantly, identifying the source of the problem and the means to work around it, issues that we’ve been struggling with for all these decades, is remarkable.”

    Says James M. LeBeau, the John Chipman Associate Professor of Materials Science and Engineering at MIT, who was also involved in the research, “This work is important because it could overcome [some] of the limitations that have prevented the widespread use of solid oxide fuel cells. Additionally, the basic concept can be applied to many other materials used for applications in the energy-related field.”

    A report describing the work was reported Aug. 11, in Energy & Environmental Science. Additional authors of the paper are Han Gil Seo, a DMSE postdoc; Anna Staerz, formerly a DMSE postdoc, now at Interuniversity Microelectronics Centre (IMEC) Belgium and soon to join the Colorado School of Mines faculty; Dennis S. Kim, a DMSE postdoc; Dino Klotz, a DMSE visiting scientist, now at Zurich Instruments; Michael Xu, a DMSE graduate student; and Clement Nicollet, formerly a DMSE postdoc, now at the Université de Nantes. Seo and Staerz contributed equally to the work.

    Changing the acidity

    A fuel/electrolysis cell has three principal parts: two electrodes (a cathode and anode) separated by an electrolyte. In the electrolysis mode, electricity from, say, the wind, can be used to generate storable fuel like methane or hydrogen. On the other hand, in the reverse fuel cell reaction, that storable fuel can be used to create electricity when the wind isn’t blowing.

    A working fuel/electrolysis cell is composed of many individual cells that are stacked together and connected by steel metal interconnects that include the element chrome to keep the metal from oxidizing. But “it turns out that at the high temperatures that these cells run, some of that chrome evaporates and migrates to the interface between the cathode and the electrolyte, poisoning the oxygen incorporation reaction,” Tuller says. After a certain point, the efficiency of the cell has dropped to a point where it is not worth operating any longer.

    “So if you can extend the life of the fuel/electrolysis cell by slowing down this process, or ideally reversing it, you could go a long way towards making it practical,” Tuller says.

    The team showed that you can do both by controlling the acidity of the cathode surface. They also explained what is happening.

    To achieve their results, the team coated the fuel/electrolysis cell cathode with lithium oxide, a compound that changes the relative acidity of the surface from being acidic to being more basic. “After adding a small amount of lithium, we were able to recover the initial performance of a poisoned cell,” Tuller says. When the engineers added even more lithium, the performance improved far beyond the initial value. “We saw improvements of three to four orders of magnitude in the key oxygen reduction reaction rate and attribute the change to populating the surface of the electrode with electrons needed to drive the oxygen incorporation reaction.”

    The engineers went on to explain what is happening by observing the material at the nanoscale, or billionths of a meter, with state-of-the-art transmission electron microscopy and electron energy loss spectroscopy at MIT.nano. “We were interested in understanding the distribution of the different chemical additives [chromium and lithium oxide] on the surface,” says LeBeau.

    They found that the lithium oxide effectively dissolves the chromium to form a glassy material that no longer serves to degrade the cathode performance.

    Applications for sensors, catalysts, and more

    Many technologies like fuel cells are based on the ability of the oxide solids to rapidly breathe oxygen in and out of their crystalline structures, Tuller says. The MIT work essentially shows how to recover — and speed up — that ability by changing the surface acidity. As a result, the engineers are optimistic that the work could be applied to other technologies including, for example, sensors, catalysts, and oxygen permeation-based reactors.

    The team is also exploring the effect of acidity on systems poisoned by different elements, like silica.

    Concludes Tuller: “As is often the case in science, you stumble across something and notice an important trend that was not appreciated previously. Then you test that concept further, and you discover that it is really very fundamental.”

    In addition to the DOE, this work was also funded by the National Research Foundation of Korea, the MIT Department of Materials Science and Engineering via Tuller’s appointment as the R.P. Simmons Professor of Ceramics and Electronic Materials, and the U.S. Air Force Office of Scientific Research. More

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    New hardware offers faster computation for artificial intelligence, with much less energy

    As scientists push the boundaries of machine learning, the amount of time, energy, and money required to train increasingly complex neural network models is skyrocketing. A new area of artificial intelligence called analog deep learning promises faster computation with a fraction of the energy usage.

    Programmable resistors are the key building blocks in analog deep learning, just like transistors are the core elements for digital processors. By repeating arrays of programmable resistors in complex layers, researchers can create a network of analog artificial “neurons” and “synapses” that execute computations just like a digital neural network. This network can then be trained to achieve complex AI tasks like image recognition and natural language processing.

    A multidisciplinary team of MIT researchers set out to push the speed limits of a type of human-made analog synapse that they had previously developed. They utilized a practical inorganic material in the fabrication process that enables their devices to run 1 million times faster than previous versions, which is also about 1 million times faster than the synapses in the human brain.

    Moreover, this inorganic material also makes the resistor extremely energy-efficient. Unlike materials used in the earlier version of their device, the new material is compatible with silicon fabrication techniques. This change has enabled fabricating devices at the nanometer scale and could pave the way for integration into commercial computing hardware for deep-learning applications.

    “With that key insight, and the very powerful nanofabrication techniques we have at MIT.nano, we have been able to put these pieces together and demonstrate that these devices are intrinsically very fast and operate with reasonable voltages,” says senior author Jesús A. del Alamo, the Donner Professor in MIT’s Department of Electrical Engineering and Computer Science (EECS). “This work has really put these devices at a point where they now look really promising for future applications.”

    “The working mechanism of the device is electrochemical insertion of the smallest ion, the proton, into an insulating oxide to modulate its electronic conductivity. Because we are working with very thin devices, we could accelerate the motion of this ion by using a strong electric field, and push these ionic devices to the nanosecond operation regime,” explains senior author Bilge Yildiz, the Breene M. Kerr Professor in the departments of Nuclear Science and Engineering and Materials Science and Engineering.

    “The action potential in biological cells rises and falls with a timescale of milliseconds, since the voltage difference of about 0.1 volt is constrained by the stability of water,” says senior author Ju Li, the Battelle Energy Alliance Professor of Nuclear Science and Engineering and professor of materials science and engineering, “Here we apply up to 10 volts across a special solid glass film of nanoscale thickness that conducts protons, without permanently damaging it. And the stronger the field, the faster the ionic devices.”

    These programmable resistors vastly increase the speed at which a neural network is trained, while drastically reducing the cost and energy to perform that training. This could help scientists develop deep learning models much more quickly, which could then be applied in uses like self-driving cars, fraud detection, or medical image analysis.

    “Once you have an analog processor, you will no longer be training networks everyone else is working on. You will be training networks with unprecedented complexities that no one else can afford to, and therefore vastly outperform them all. In other words, this is not a faster car, this is a spacecraft,” adds lead author and MIT postdoc Murat Onen.

    Co-authors include Frances M. Ross, the Ellen Swallow Richards Professor in the Department of Materials Science and Engineering; postdocs Nicolas Emond and Baoming Wang; and Difei Zhang, an EECS graduate student. The research is published today in Science.

    Accelerating deep learning

    Analog deep learning is faster and more energy-efficient than its digital counterpart for two main reasons. “First, computation is performed in memory, so enormous loads of data are not transferred back and forth from memory to a processor.” Analog processors also conduct operations in parallel. If the matrix size expands, an analog processor doesn’t need more time to complete new operations because all computation occurs simultaneously.

    The key element of MIT’s new analog processor technology is known as a protonic programmable resistor. These resistors, which are measured in nanometers (one nanometer is one billionth of a meter), are arranged in an array, like a chess board.

    In the human brain, learning happens due to the strengthening and weakening of connections between neurons, called synapses. Deep neural networks have long adopted this strategy, where the network weights are programmed through training algorithms. In the case of this new processor, increasing and decreasing the electrical conductance of protonic resistors enables analog machine learning.

    The conductance is controlled by the movement of protons. To increase the conductance, more protons are pushed into a channel in the resistor, while to decrease conductance protons are taken out. This is accomplished using an electrolyte (similar to that of a battery) that conducts protons but blocks electrons.

    To develop a super-fast and highly energy efficient programmable protonic resistor, the researchers looked to different materials for the electrolyte. While other devices used organic compounds, Onen focused on inorganic phosphosilicate glass (PSG).

    PSG is basically silicon dioxide, which is the powdery desiccant material found in tiny bags that come in the box with new furniture to remove moisture. It is studied as a proton conductor under humidified conditions for fuel cells. It is also the most well-known oxide used in silicon processing. To make PSG, a tiny bit of phosphorus is added to the silicon to give it special characteristics for proton conduction.

    Onen hypothesized that an optimized PSG could have a high proton conductivity at room temperature without the need for water, which would make it an ideal solid electrolyte for this application. He was right.

    Surprising speed

    PSG enables ultrafast proton movement because it contains a multitude of nanometer-sized pores whose surfaces provide paths for proton diffusion. It can also withstand very strong, pulsed electric fields. This is critical, Onen explains, because applying more voltage to the device enables protons to move at blinding speeds.

    “The speed certainly was surprising. Normally, we would not apply such extreme fields across devices, in order to not turn them into ash. But instead, protons ended up shuttling at immense speeds across the device stack, specifically a million times faster compared to what we had before. And this movement doesn’t damage anything, thanks to the small size and low mass of protons. It is almost like teleporting,” he says.

    “The nanosecond timescale means we are close to the ballistic or even quantum tunneling regime for the proton, under such an extreme field,” adds Li.

    Because the protons don’t damage the material, the resistor can run for millions of cycles without breaking down. This new electrolyte enabled a programmable protonic resistor that is a million times faster than their previous device and can operate effectively at room temperature, which is important for incorporating it into computing hardware.

    Thanks to the insulating properties of PSG, almost no electric current passes through the material as protons move. This makes the device extremely energy efficient, Onen adds.

    Now that they have demonstrated the effectiveness of these programmable resistors, the researchers plan to reengineer them for high-volume manufacturing, says del Alamo. Then they can study the properties of resistor arrays and scale them up so they can be embedded into systems.

    At the same time, they plan to study the materials to remove bottlenecks that limit the voltage that is required to efficiently transfer the protons to, through, and from the electrolyte.

    “Another exciting direction that these ionic devices can enable is energy-efficient hardware to emulate the neural circuits and synaptic plasticity rules that are deduced in neuroscience, beyond analog deep neural networks. We have already started such a collaboration with neuroscience, supported by the MIT Quest for Intelligence,” adds Yildiz.

    “The collaboration that we have is going to be essential to innovate in the future. The path forward is still going to be very challenging, but at the same time it is very exciting,” del Alamo says.

    “Intercalation reactions such as those found in lithium-ion batteries have been explored extensively for memory devices. This work demonstrates that proton-based memory devices deliver impressive and surprising switching speed and endurance,” says William Chueh, associate professor of materials science and engineering at Stanford University, who was not involved with this research. “It lays the foundation for a new class of memory devices for powering deep learning algorithms.”

    “This work demonstrates a significant breakthrough in biologically inspired resistive-memory devices. These all-solid-state protonic devices are based on exquisite atomic-scale control of protons, similar to biological synapses but at orders of magnitude faster rates,” says Elizabeth Dickey, the Teddy & Wilton Hawkins Distinguished Professor and head of the Department of Materials Science and Engineering at Carnegie Mellon University, who was not involved with this work. “I commend the interdisciplinary MIT team for this exciting development, which will enable future-generation computational devices.”

    This research is funded, in part, by the MIT-IBM Watson AI Lab. More

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    MIT engineers design surfaces that make water boil more efficiently

    The boiling of water or other fluids is an energy-intensive step at the heart of a wide range of industrial processes, including most electricity generating plants, many chemical production systems, and even cooling systems for electronics.

    Improving the efficiency of systems that heat and evaporate water could significantly reduce their energy use. Now, researchers at MIT have found a way to do just that, with a specially tailored surface treatment for the materials used in these systems.

    The improved efficiency comes from a combination of three different kinds of surface modifications, at different size scales. The new findings are described in the journal Advanced Materials in a paper by recent MIT graduate Youngsup Song PhD ’21, Ford Professor of Engineering Evelyn Wang, and four others at MIT. The researchers note that this initial finding is still at a laboratory scale, and more work is needed to develop a practical, industrial-scale process.

    There are two key parameters that describe the boiling process: the heat transfer coefficient (HTC) and the critical heat flux (CHF). In materials design, there’s generally a tradeoff between the two, so anything that improves one of these parameters tends to make the other worse. But both are important for the efficiency of the system, and now, after years of work, the team has achieved a way of significantly improving both properties at the same time, through their combination of different textures added to a material’s surface.

    “Both parameters are important,” Song says, “but enhancing both parameters together is kind of tricky because they have intrinsic trade off.” The reason for that, he explains, is “because if we have lots of bubbles on the boiling surface, that means boiling is very efficient, but if we have too many bubbles on the surface, they can coalesce together, which can form a vapor film over the boiling surface.” That film introduces resistance to the heat transfer from the hot surface to the water. “If we have vapor in between the surface and water, that prevents the heat transfer efficiency and lowers the CHF value,” he says.

    Song, who is now a postdoc at Lawrence Berkeley National Laboratory, carried out much of the research as part of his doctoral thesis work at MIT. While the various components of the new surface treatment he developed had been previously studied, the researchers say this work is the first to show that these methods could be combined to overcome the tradeoff between the two competing parameters.

    Adding a series of microscale cavities, or dents, to a surface is a way of controlling the way bubbles form on that surface, keeping them effectively pinned to the locations of the dents and preventing them from spreading out into a heat-resisting film. In this work, the researchers created an array of 10-micrometer-wide dents separated by about 2 millimeters to prevent film formation. But that separation also reduces the concentration of bubbles at the surface, which can reduce the boiling efficiency. To compensate for that, the team introduced a much smaller-scale surface treatment, creating tiny bumps and ridges at the nanometer scale, which increases the surface area and promotes the rate of evaporation under the bubbles.

    In these experiments, the cavities were made in the centers of a series of pillars on the material’s surface. These pillars, combined with nanostructures, promote wicking of liquid from the base to their tops, and this enhances the boiling process by providing more surface area exposed to the water. In combination, the three “tiers” of the surface texture — the cavity separation, the posts, and the nanoscale texturing — provide a greatly enhanced efficiency for the boiling process, Song says.

    “Those micro cavities define the position where bubbles come up,” he says. “But by separating those cavities by 2 millimeters, we separate the bubbles and minimize the coalescence of bubbles.” At the same time, the nanostructures promote evaporation under the bubbles, and the capillary action induced by the pillars supplies liquid to the bubble base. That maintains a layer of liquid water between the boiling surface and the bubbles of vapor, which enhances the maximum heat flux.

    Although their work has confirmed that the combination of these kinds of surface treatments can work and achieve the desired effects, this work was done under small-scale laboratory conditions that could not easily be scaled up to practical devices, Wang says. “These kinds of structures we’re making are not meant to be scaled in its current form,” she says, but rather were used to prove that such a system can work. One next step will be to find alternative ways of creating these kinds of surface textures so these methods could more easily be scaled up to practical dimensions.

    “Showing that we can control the surface in this way to get enhancement is a first step,” she says. “Then the next step is to think about more scalable approaches.” For example, though the pillars on the surface in these experiments were created using clean-room methods commonly used to produce semiconductor chips, there are other, less demanding ways of creating such structures, such as electrodeposition. There are also a number of different ways to produce the surface nanostructure textures, some of which may be more easily scalable.

    There may be some significant small-scale applications that could use this process in its present form, such as the thermal management of electronic devices, an area that is becoming more important as semiconductor devices get smaller and managing their heat output becomes ever more important. “There’s definitely a space there where this is really important,” Wang says.

    Even those kinds of applications will take some time to develop because typically thermal management systems for electronics use liquids other than water, known as dielectric liquids. These liquids have different surface tension and other properties than water, so the dimensions of the surface features would have to be adjusted accordingly. Work on these differences is one of the next steps for the ongoing research, Wang says.

    This same multiscale structuring technique could also be applied to different liquids, Song says, by adjusting the dimensions to account for the different properties of the liquids. “Those kinds of details can be changed, and that can be our next step,” he says.

    The team also included Carlos Diaz-Martin, Lenan Zhang, Hyeongyun Cha, and Yajing Zhao, all at MIT. The work was supported by the Advanced Research Projects Agency-Energy (ARPA-E), the Air Force Office of Scientific Research, and the Singapore-MIT Alliance for Research and Technology, and made use of the MIT.nano facilities. More

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    New program bolsters innovation in next-generation artificial intelligence hardware

    The MIT AI Hardware Program is a new academia and industry collaboration aimed at defining and developing translational technologies in hardware and software for the AI and quantum age. A collaboration between the MIT School of Engineering and MIT Schwarzman College of Computing, involving the Microsystems Technologies Laboratories and programs and units in the college, the cross-disciplinary effort aims to innovate technologies that will deliver enhanced energy efficiency systems for cloud and edge computing.

    “A sharp focus on AI hardware manufacturing, research, and design is critical to meet the demands of the world’s evolving devices, architectures, and systems,” says Anantha Chandrakasan, dean of the MIT School of Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science. “Knowledge-sharing between industry and academia is imperative to the future of high-performance computing.”

    Based on use-inspired research involving materials, devices, circuits, algorithms, and software, the MIT AI Hardware Program convenes researchers from MIT and industry to facilitate the transition of fundamental knowledge to real-world technological solutions. The program spans materials and devices, as well as architecture and algorithms enabling energy-efficient and sustainable high-performance computing.

    “As AI systems become more sophisticated, new solutions are sorely needed to enable more advanced applications and deliver greater performance,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and Henry Ellis Warren Professor of Electrical Engineering and Computer Science. “Our aim is to devise real-world technological solutions and lead the development of technologies for AI in hardware and software.”

    The inaugural members of the program are companies from a wide range of industries including chip-making, semiconductor manufacturing equipment, AI and computing services, and information systems R&D organizations. The companies represent a diverse ecosystem, both nationally and internationally, and will work with MIT faculty and students to help shape a vibrant future for our planet through cutting-edge AI hardware research.

    The five inaugural members of the MIT AI Hardware Program are:  

    Amazon, a global technology company whose hardware inventions include the Kindle, Amazon Echo, Fire TV, and Astro; 
    Analog Devices, a global leader in the design and manufacturing of analog, mixed signal, and DSP integrated circuits; 
    ASML, an innovation leader in the semiconductor industry, providing chipmakers with hardware, software, and services to mass produce patterns on silicon through lithography; 
    NTT Research, a subsidiary of NTT that conducts fundamental research to upgrade reality in game-changing ways that improve lives and brighten our global future; and 
    TSMC, the world’s leading dedicated semiconductor foundry.

    The MIT AI Hardware Program will create a roadmap of transformative AI hardware technologies. Leveraging MIT.nano, the most advanced university nanofabrication facility anywhere, the program will foster a unique environment for AI hardware research.  

    “We are all in awe at the seemingly superhuman capabilities of today’s AI systems. But this comes at a rapidly increasing and unsustainable energy cost,” says Jesús del Alamo, the Donner Professor in MIT’s Department of Electrical Engineering and Computer Science. “Continued progress in AI will require new and vastly more energy-efficient systems. This, in turn, will demand innovations across the entire abstraction stack, from materials and devices to systems and software. The program is in a unique position to contribute to this quest.”

    The program will prioritize the following topics:

    analog neural networks;
    new roadmap CMOS designs;
    heterogeneous integration for AI systems;
    onolithic-3D AI systems;
    analog nonvolatile memory devices;
    software-hardware co-design;
    intelligence at the edge;
    intelligent sensors;
    energy-efficient AI;
    intelligent internet of things (IIoT);
    neuromorphic computing;
    AI edge security;
    quantum AI;
    wireless technologies;
    hybrid-cloud computing; and
    high-performance computation.

    “We live in an era where paradigm-shifting discoveries in hardware, systems communications, and computing have become mandatory to find sustainable solutions — solutions that we are proud to give to the world and generations to come,” says Aude Oliva, senior research scientist in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and director of strategic industry engagement in the MIT Schwarzman College of Computing.

    The new program is co-led by Jesús del Alamo and Aude Oliva, and Anantha Chandrakasan serves as chair. More