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    Two MIT teams selected for NSF sustainable materials grants

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    Two teams led by MIT researchers were selected in December 2023 by the U.S. National Science Foundation (NSF) Convergence Accelerator, a part of the TIP Directorate, to receive awards of $5 million each over three years, to pursue research aimed at helping to bring cutting-edge new sustainable materials and processes from the lab into practical, full-scale industrial production. The selection was made after 16 teams from around the country were chosen last year for one-year grants to develop detailed plans for further research aimed at solving problems of sustainability and scalability for advanced electronic products.

    Of the two MIT-led teams chosen for this current round of funding, one team, Topological Electric, is led by Mingda Li, an associate professor in the Department of Nuclear Science and Engineering. This team will be finding pathways to scale up sustainable topological materials, which have the potential to revolutionize next-generation microelectronics by showing superior electronic performance, such as dissipationless states or high-frequency response. The other team, led by Anuradha Agarwal, a principal research scientist at MIT’s Materials Research Laboratory, will be focusing on developing new materials, devices, and manufacturing processes for microchips that minimize energy consumption using electronic-photonic integration, and that detect and avoid the toxic or scarce materials used in today’s production methods.

    Scaling the use of topological materials

    Li explains that some materials based on quantum effects have achieved successful transitions from lab curiosities to successful mass production, such as blue-light LEDs, and giant magnetorestance (GMR) devices used for magnetic data storage. But he says there are a variety of equally promising materials that have shown promise but have yet to make it into real-world applications.

    “What we really wanted to achieve is to bring newer-generation quantum materials into technology and mass production, for the benefit of broader society,” he says. In particular, he says, “topological materials are really promising to do many different things.”

    Topological materials are ones whose electronic properties are fundamentally protected against disturbance. For example, Li points to the fact that just in the last two years, it has been shown that some topological materials are even better electrical conductors than copper, which are typically used for the wires interconnecting electronic components. But unlike the blue-light LEDs or the GMR devices, which have been widely produced and deployed, when it comes to topological materials, “there’s no company, no startup, there’s really no business out there,” adds Tomas Palacios, the Clarence J. Lebel Professor in Electrical Engineering at MIT and co-principal investigator on Li’s team. Part of the reason is that many versions of such materials are studied “with a focus on fundamental exotic physical properties with little or no consideration on the sustainability aspects,” says Liang Fu, an MIT professor of physics and also a co-PI. Their team will be looking for alternative formulations that are more amenable to mass production.

    One possible application of these topological materials is for detecting terahertz radiation, explains Keith Nelson, an MIT professor of chemistry and co-PI. This extremely high-frequency electronics can carry far more information than conventional radio or microwaves, but at present there are no mature electronic devices available that are scalable at this frequency range. “There’s a whole range of possibilities for topological materials” that could work at these frequencies, he says. In addition, he says, “we hope to demonstrate an entire prototype system like this in a single, very compact solid-state platform.”

    Li says that among the many possible applications of topological devices for microelectronics devices of various kinds, “we don’t know which, exactly, will end up as a product, or will reach real industrial scaleup. That’s why this opportunity from NSF is like a bridge, which is precious, to allow us to dig deeper to unleash the true potential.”

    In addition to Li, Palacios, Fu, and Nelson, the Topological Electric team includes Qiong Ma, assistant professor of physics in Boston College; Farnaz Niroui, assistant professor of electrical engineering and computer science at MIT; Susanne Stemmer, professor of materials at the University of California at Santa Barbara; Judy Cha, professor of materials science and engineering at Cornell University; industrial partners including IBM, Analog Devices, and Raytheon; and professional consultants. “We are taking this opportunity seriously,” Li says. “We really want to see if the topological materials are as good as we show in the lab when being scaled up, and how far we can push to broadly industrialize them.”

    Toward sustainable microchip production and use

    The microchips behind everything from smartphones to medical imaging are associated with a significant percentage of greenhouse gas emissions today, and every year the world produces more than 50 million metric tons of electronic waste, the equivalent of about 5,000 Eiffel Towers. Further, the data centers necessary for complex computations and huge amount of data transfer — think AI and on-demand video — are growing and will require 10 percent of the world’s electricity by 2030.

    “The current microchip manufacturing supply chain, which includes production, distribution, and use, is neither scalable nor sustainable, and cannot continue. We must innovate our way out of this crisis,” says Agarwal.

    The name of Agarwal’s team, FUTUR-IC, is a reference to the future of the integrated circuits, or chips, through a global alliance for sustainable microchip manufacturing. Says Agarwal, “We bring together stakeholders from industry, academia, and government to co-optimize across three dimensions: technology, ecology, and workforce. These were identified as key interrelated areas by some 140 stakeholders. With FUTUR-IC we aim to cut waste and CO2-equivalent emissions associated with electronics by 50 percent every 10 years.”

    The market for microelectronics in the next decade is predicted to be on the order of a trillion dollars, but most of the manufacturing for the industry occurs only in limited geographical pockets around the world. FUTUR-IC aims to diversify and strengthen the supply chain for manufacturing and packaging of electronics. The alliance has 26 collaborators and is growing. Current external collaborators include the International Electronics Manufacturing Initiative (iNEMI), Tyndall National Institute, SEMI, Hewlett Packard Enterprise, Intel, and the Rochester Institute of Technology.

    Agarwal leads FUTUR-IC in close collaboration with others, including, from MIT, Lionel Kimerling, the Thomas Lord Professor of Materials Science and Engineering; Elsa Olivetti, the Jerry McAfee Professor in Engineering; Randolph Kirchain, principal research scientist in the Materials Research Laboratory; and Greg Norris, director of MIT’s Sustainability and Health Initiative for NetPositive Enterprise (SHINE). All are affiliated with the Materials Research Laboratory. They are joined by Samuel Serna, an MIT visiting professor and assistant professor of physics at Bridgewater State University. Other key personnel include Sajan Saini, education director for the Initiative for Knowledge and Innovation in Manufacturing in MIT’s Department of Materials Science and Engineering; Peter O’Brien, a professor from Tyndall National Institute; and Shekhar Chandrashekhar, CEO of iNEMI.

    “We expect the integration of electronics and photonics to revolutionize microchip manufacturing, enhancing efficiency, reducing energy consumption, and paving the way for unprecedented advancements in computing speed and data-processing capabilities,” says Serna, who is the co-lead on the project’s technology “vector.”

    Common metrics for these efforts are needed, says Norris, co-lead for the ecology vector, adding, “The microchip industry must have transparent and open Life Cycle Assessment (LCA) models and data, which are being developed by FUTUR-IC.” This is especially important given that microelectronics production transcends industries. “Given the scale and scope of microelectronics, it is critical for the industry to lead in the transition to sustainable manufacture and use,” says Kirchain, another co-lead and the co-director of the Concrete Sustainability Hub at MIT. To bring about this cross-fertilization, co-lead Olivetti, also co-director of the MIT Climate and Sustainability Consortium (MCSC), will collaborate with FUTUR-IC to enhance the benefits from microchip recycling, leveraging the learning across industries.

    Saini, the co-lead for the workforce vector, stresses the need for agility. “With a workforce that adapts to a practice of continuous upskilling, we can help increase the robustness of the chip-manufacturing supply chain, and validate a new design for a sustainability curriculum,” he says.

    “We have become accustomed to the benefits forged by the exponential growth of microelectronic technology performance and market size,” says Kimerling, who is also director of MIT’s Materials Research Laboratory and co-director of the MIT Microphotonics Center. “The ecological impact of this growth in terms of materials use, energy consumption and end-of-life disposal has begun to push back against this progress. We believe that concurrently engineered solutions for these three dimensions will build a common learning curve to power the next 40 years of progress in the semiconductor industry.”

    The MIT teams are two of six that received awards addressing sustainable materials for global challenges through phase two of the NSF Convergence Accelerator program. Launched in 2019, the program targets solutions to especially compelling challenges at an accelerated pace by incorporating a multidisciplinary research approach. More

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      in Energy

      Propelling atomically layered magnets toward green computers

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      Globally, computation is booming at an unprecedented rate, fueled by the boons of artificial intelligence. With this, the staggering energy demand of the world’s computing infrastructure has become a major concern, and the development of computing devices that are far more energy-efficient is a leading challenge for the scientific community. 

      Use of magnetic materials to build computing devices like memories and processors has emerged as a promising avenue for creating “beyond-CMOS” computers, which would use far less energy compared to traditional computers. Magnetization switching in magnets can be used in computation the same way that a transistor switches from open or closed to represent the 0s and 1s of binary code. 

      While much of the research along this direction has focused on using bulk magnetic materials, a new class of magnetic materials — called two-dimensional van der Waals magnets — provides superior properties that can improve the scalability and energy efficiency of magnetic devices to make them commercially viable. 

      Although the benefits of shifting to 2D magnetic materials are evident, their practical induction into computers has been hindered by some fundamental challenges. Until recently, 2D magnetic materials could operate only at very low temperatures, much like superconductors. So bringing their operating temperatures above room temperature has remained a primary goal. Additionally, for use in computers, it is important that they can be controlled electrically, without the need for magnetic fields. Bridging this fundamental gap, where 2D magnetic materials can be electrically switched above room temperature without any magnetic fields, could potentially catapult the translation of 2D magnets into the next generation of “green” computers.

      A team of MIT researchers has now achieved this critical milestone by designing a “van der Waals atomically layered heterostructure” device where a 2D van der Waals magnet, iron gallium telluride, is interfaced with another 2D material, tungsten ditelluride. In an open-access paper published March 15 in Science Advances, the team shows that the magnet can be toggled between the 0 and 1 states simply by applying pulses of electrical current across their two-layer device. 

      Play video

      The Future of Spintronics: Manipulating Spins in Atomic Layers without External Magnetic FieldsVideo: Deblina Sarkar

      “Our device enables robust magnetization switching without the need for an external magnetic field, opening up unprecedented opportunities for ultra-low power and environmentally sustainable computing technology for big data and AI,” says lead author Deblina Sarkar, the AT&T Career Development Assistant Professor at the MIT Media Lab and Center for Neurobiological Engineering, and head of the Nano-Cybernetic Biotrek research group. “Moreover, the atomically layered structure of our device provides unique capabilities including improved interface and possibilities of gate voltage tunability, as well as flexible and transparent spintronic technologies.”

      Sarkar is joined on the paper by first author Shivam Kajale, a graduate student in Sarkar’s research group at the Media Lab; Thanh Nguyen, a graduate student in the Department of Nuclear Science and Engineering (NSE); Nguyen Tuan Hung, an MIT visiting scholar in NSE and an assistant professor at Tohoku University in Japan; and Mingda Li, associate professor of NSE.

      Breaking the mirror symmetries 

      When electric current flows through heavy metals like platinum or tantalum, the electrons get segregated in the materials based on their spin component, a phenomenon called the spin Hall effect, says Kajale. The way this segregation happens depends on the material, and particularly its symmetries.

      “The conversion of electric current to spin currents in heavy metals lies at the heart of controlling magnets electrically,” Kajale notes. “The microscopic structure of conventionally used materials, like platinum, have a kind of mirror symmetry, which restricts the spin currents only to in-plane spin polarization.”

      Kajale explains that two mirror symmetries must be broken to produce an “out-of-plane” spin component that can be transferred to a magnetic layer to induce field-free switching. “Electrical current can ‘break’ the mirror symmetry along one plane in platinum, but its crystal structure prevents the mirror symmetry from being broken in a second plane.”

      In their earlier experiments, the researchers used a small magnetic field to break the second mirror plane. To get rid of the need for a magnetic nudge, Kajale and Sarkar and colleagues looked instead for a material with a structure that could break the second mirror plane without outside help. This led them to another 2D material, tungsten ditelluride. The tungsten ditelluride that the researchers used has an orthorhombic crystal structure. The material itself has one broken mirror plane. Thus, by applying current along its low-symmetry axis (parallel to the broken mirror plane), the resulting spin current has an out-of-plane spin component that can directly induce switching in the ultra-thin magnet interfaced with the tungsten ditelluride. 

      “Because it’s also a 2D van der Waals material, it can also ensure that when we stack the two materials together, we get pristine interfaces and a good flow of electron spins between the materials,” says Kajale. 

      Becoming more energy-efficient 

      Computer memory and processors built from magnetic materials use less energy than traditional silicon-based devices. And the van der Waals magnets can offer higher energy efficiency and better scalability compared to bulk magnetic material, the researchers note. 

      The electrical current density used for switching the magnet translates to how much energy is dissipated during switching. A lower density means a much more energy-efficient material. “The new design has one of the lowest current densities in van der Waals magnetic materials,” Kajale says. “This new design has an order of magnitude lower in terms of the switching current required in bulk materials. This translates to something like two orders of magnitude improvement in energy efficiency.”

      The research team is now looking at similar low-symmetry van der Waals materials to see if they can reduce current density even further. They are also hoping to collaborate with other researchers to find ways to manufacture the 2D magnetic switch devices at commercial scale. 

      This work was carried out, in part, using the facilities at MIT.nano. It was funded by the Media Lab, the U.S. National Science Foundation, and the U.S. Department of Energy. More

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      in Environment

      MIT-led teams win National Science Foundation grants to research sustainable materials

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      Three MIT-led teams are among 16 nationwide to receive funding awards to address sustainable materials for global challenges through the National Science Foundation’s Convergence Accelerator program. Launched in 2019, the program targets solutions to especially compelling societal or scientific challenges at an accelerated pace, by incorporating a multidisciplinary research approach.

      “Solutions for today’s national-scale societal challenges are hard to solve within a single discipline. Instead, these challenges require convergence to merge ideas, approaches, and technologies from a wide range of diverse sectors, disciplines, and experts,” the NSF explains in its description of the Convergence Accelerator program. Phase 1 of the award involves planning to expand initial concepts, identify new team members, participate in an NSF development curriculum, and create an early prototype.

      Sustainable microchips

      One of the funded projects, “Building a Sustainable, Innovative Ecosystem for Microchip Manufacturing,” will be led by Anuradha Murthy Agarwal, a principal research scientist at the MIT Materials Research Laboratory. The aim of this project is to help transition the manufacturing of microchips to more sustainable processes that, for example, can reduce e-waste landfills by allowing repair of chips, or enable users to swap out a rogue chip in a motherboard rather than tossing out the entire laptop or cellphone.

      “Our goal is to help transition microchip manufacturing towards a sustainable industry,” says Agarwal. “We aim to do that by partnering with industry in a multimodal approach that prototypes technology designs to minimize energy consumption and waste generation, retrains the semiconductor workforce, and creates a roadmap for a new industrial ecology to mitigate materials-critical limitations and supply-chain constraints.”

      Agarwal’s co-principal investigators are Samuel Serna, an MIT visiting professor and assistant professor of physics at Bridgewater State University, and two MIT faculty affiliated with the Materials Research Laboratory: Juejun Hu, the John Elliott Professor of Materials Science and Engineering; and Lionel Kimerling, the Thomas Lord Professor of Materials Science and Engineering.

      The training component of the project will also create curricula for multiple audiences. “At Bridgewater State University, we will create a new undergraduate course on microchip manufacturing sustainability, and eventually adapt it for audiences from K-12, as well as incumbent employees,” says Serna.

      Sajan Saini and Erik Verlage of the MIT Department of Materials Science and Engineering (DMSE), and Randolph Kirchain from the MIT Materials Systems Laboratory, who have led MIT initiatives in virtual reality digital education, materials criticality, and roadmapping, are key contributors. The project also includes DMSE graduate students Drew Weninger and Luigi Ranno, and undergraduate Samuel Bechtold from Bridgewater State University’s Department of Physics.

      Sustainable topological materials

      Under the direction of Mingda Li, the Class of 1947 Career Development Professor and an Associate Professor of Nuclear Science and Engineering, the “Sustainable Topological Energy Materials (STEM) for Energy-efficient Applications” project will accelerate research in sustainable topological quantum materials.

      Topological materials are ones that retain a particular property through all external disturbances. Such materials could potentially be a boon for quantum computing, which has so far been plagued by instability, and would usher in a post-silicon era for microelectronics. Even better, says Li, topological materials can do their job without dissipating energy even at room temperatures.

      Topological materials can find a variety of applications in quantum computing, energy harvesting, and microelectronics. Despite their promise, and a few thousands of potential candidates, discovery and mass production of these materials has been challenging. Topology itself is not a measurable characteristic so researchers have to first develop ways to find hints of it. Synthesis of materials and related process optimization can take months, if not years, Li adds. Machine learning can accelerate the discovery and vetting stage.

      Given that a best-in-class topological quantum material has the potential to disrupt the semiconductor and computing industries, Li and team are paying special attention to the environmental sustainability of prospective materials. For example, some potential candidates include gold, lead, or cadmium, whose scarcity or toxicity does not lend itself to mass production and have been disqualified.

      Co-principal investigators on the project include Liang Fu, associate professor of physics at MIT; Tomas Palacios, professor of electrical engineering and computer science at MIT and director of the Microsystems Technology Laboratories; Susanne Stemmer of the University of California at Santa Barbara; and Qiong Ma of Boston College. The $750,000 one-year Phase 1 grant will focus on three priorities: building a topological materials database; identifying the most environmentally sustainable candidates for energy-efficient topological applications; and building the foundation for a Center for Sustainable Topological Energy Materials at MIT that will encourage industry-academia collaborations.

      At a time when the size of silicon-based electronic circuit boards is reaching its lower limit, the promise of topological materials whose conductivity increases with decreasing size is especially attractive, Li says. In addition, topological materials can harvest wasted heat: Imagine using your body heat to power your phone. “There are different types of application scenarios, and we can go much beyond the capabilities of existing materials,” Li says, “the possibilities of topological materials are endlessly exciting.”

      Socioresilient materials design

      Researchers in the MIT Department of Materials Science and Engineering (DMSE) have been awarded $750,000 in a cross-disciplinary project that aims to fundamentally redirect materials research and development toward more environmentally, socially, and economically sustainable and resilient materials. This “socioresilient materials design” will serve as the foundation for a new research and development framework that takes into account technical, environmental, and social factors from the beginning of the materials design and development process.

      Christine Ortiz, the Morris Cohen Professor of Materials Science and Engineering, and Ellan Spero PhD ’14, an instructor in DMSE, are leading this research effort, which includes Cornell University, the University of Swansea, Citrine Informatics, Station1, and 14 other organizations in academia, industry, venture capital, the social sector, government, and philanthropy.

      The team’s project, “Mind Over Matter: Socioresilient Materials Design,” emphasizes that circular design approaches, which aim to minimize waste and maximize the reuse, repair, and recycling of materials, are often insufficient to address negative repercussions for the planet and for human health and safety.

      Too often society understands the unintended negative consequences long after the materials that make up our homes and cities and systems have been in production and use for many years. Examples include disparate and negative public health impacts due to industrial scale manufacturing of materials, water and air contamination with harmful materials, and increased risk of fire in lower-income housing buildings due to flawed materials usage and design. Adverse climate events including drought, flood, extreme temperatures, and hurricanes have accelerated materials degradation, for example in critical infrastructure, leading to amplified environmental damage and social injustice. While classical materials design and selection approaches are insufficient to address these challenges, the new research project aims to do just that.

      “The imagination and technical expertise that goes into materials design is too often separated from the environmental and social realities of extraction, manufacturing, and end-of-life for materials,” says Ortiz. 

      Drawing on materials science and engineering, chemistry, and computer science, the project will develop a framework for materials design and development. It will incorporate powerful computational capabilities — artificial intelligence and machine learning with physics-based materials models — plus rigorous methodologies from the social sciences and the humanities to understand what impacts any new material put into production could have on society. More

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      in Energy

      Computers that power self-driving cars could be a huge driver of global carbon emissions

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      In the future, the energy needed to run the powerful computers on board a global fleet of autonomous vehicles could generate as many greenhouse gas emissions as all the data centers in the world today.

      That is one key finding of a new study from MIT researchers that explored the potential energy consumption and related carbon emissions if autonomous vehicles are widely adopted.

      The data centers that house the physical computing infrastructure used for running applications are widely known for their large carbon footprint: They currently account for about 0.3 percent of global greenhouse gas emissions, or about as much carbon as the country of Argentina produces annually, according to the International Energy Agency. Realizing that less attention has been paid to the potential footprint of autonomous vehicles, the MIT researchers built a statistical model to study the problem. They determined that 1 billion autonomous vehicles, each driving for one hour per day with a computer consuming 840 watts, would consume enough energy to generate about the same amount of emissions as data centers currently do.

      The researchers also found that in over 90 percent of modeled scenarios, to keep autonomous vehicle emissions from zooming past current data center emissions, each vehicle must use less than 1.2 kilowatts of power for computing, which would require more efficient hardware. In one scenario — where 95 percent of the global fleet of vehicles is autonomous in 2050, computational workloads double every three years, and the world continues to decarbonize at the current rate — they found that hardware efficiency would need to double faster than every 1.1 years to keep emissions under those levels.

      “If we just keep the business-as-usual trends in decarbonization and the current rate of hardware efficiency improvements, it doesn’t seem like it is going to be enough to constrain the emissions from computing onboard autonomous vehicles. This has the potential to become an enormous problem. But if we get ahead of it, we could design more efficient autonomous vehicles that have a smaller carbon footprint from the start,” says first author Soumya Sudhakar, a graduate student in aeronautics and astronautics.

      Sudhakar wrote the paper with her co-advisors Vivienne Sze, associate professor in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Research Laboratory of Electronics (RLE); and Sertac Karaman, associate professor of aeronautics and astronautics and director of the Laboratory for Information and Decision Systems (LIDS). The research appears today in the January-February issue of IEEE Micro.

      Modeling emissions

      The researchers built a framework to explore the operational emissions from computers on board a global fleet of electric vehicles that are fully autonomous, meaning they don’t require a back-up human driver.

      The model is a function of the number of vehicles in the global fleet, the power of each computer on each vehicle, the hours driven by each vehicle, and the carbon intensity of the electricity powering each computer.

      “On its own, that looks like a deceptively simple equation. But each of those variables contains a lot of uncertainty because we are considering an emerging application that is not here yet,” Sudhakar says.

      For instance, some research suggests that the amount of time driven in autonomous vehicles might increase because people can multitask while driving and the young and the elderly could drive more. But other research suggests that time spent driving might decrease because algorithms could find optimal routes that get people to their destinations faster.

      In addition to considering these uncertainties, the researchers also needed to model advanced computing hardware and software that doesn’t exist yet.

      To accomplish that, they modeled the workload of a popular algorithm for autonomous vehicles, known as a multitask deep neural network because it can perform many tasks at once. They explored how much energy this deep neural network would consume if it were processing many high-resolution inputs from many cameras with high frame rates, simultaneously.

      When they used the probabilistic model to explore different scenarios, Sudhakar was surprised by how quickly the algorithms’ workload added up.

      For example, if an autonomous vehicle has 10 deep neural networks processing images from 10 cameras, and that vehicle drives for one hour a day, it will make 21.6 million inferences each day. One billion vehicles would make 21.6 quadrillion inferences. To put that into perspective, all of Facebook’s data centers worldwide make a few trillion inferences each day (1 quadrillion is 1,000 trillion).

      “After seeing the results, this makes a lot of sense, but it is not something that is on a lot of people’s radar. These vehicles could actually be using a ton of computer power. They have a 360-degree view of the world, so while we have two eyes, they may have 20 eyes, looking all over the place and trying to understand all the things that are happening at the same time,” Karaman says.

      Autonomous vehicles would be used for moving goods, as well as people, so there could be a massive amount of computing power distributed along global supply chains, he says. And their model only considers computing — it doesn’t take into account the energy consumed by vehicle sensors or the emissions generated during manufacturing.

      Keeping emissions in check

      To keep emissions from spiraling out of control, the researchers found that each autonomous vehicle needs to consume less than 1.2 kilowatts of energy for computing. For that to be possible, computing hardware must become more efficient at a significantly faster pace, doubling in efficiency about every 1.1 years.

      One way to boost that efficiency could be to use more specialized hardware, which is designed to run specific driving algorithms. Because researchers know the navigation and perception tasks required for autonomous driving, it could be easier to design specialized hardware for those tasks, Sudhakar says. But vehicles tend to have 10- or 20-year lifespans, so one challenge in developing specialized hardware would be to “future-proof” it so it can run new algorithms.

      In the future, researchers could also make the algorithms more efficient, so they would need less computing power. However, this is also challenging because trading off some accuracy for more efficiency could hamper vehicle safety.

      Now that they have demonstrated this framework, the researchers want to continue exploring hardware efficiency and algorithm improvements. In addition, they say their model can be enhanced by characterizing embodied carbon from autonomous vehicles — the carbon emissions generated when a car is manufactured — and emissions from a vehicle’s sensors.

      While there are still many scenarios to explore, the researchers hope that this work sheds light on a potential problem people may not have considered.

      “We are hoping that people will think of emissions and carbon efficiency as important metrics to consider in their designs. The energy consumption of an autonomous vehicle is really critical, not just for extending the battery life, but also for sustainability,” says Sze.

      This research was funded, in part, by the National Science Foundation and the MIT-Accenture Fellowship. More