Evelyn Reynolds

In recent decades, China’s rapid economic growth has enabled more and more consumers to buy their own cars. The result has been improved mobility and the largest automotive market in the world — but also serious urban air pollution, high greenhouse gas emissions, and growing dependence on oil imports.

To counteract those troubling trends, the Chinese government has imposed policies to encourage the adoption of plug-in electric vehicles (EVs). Since buying an EV costs more than buying a conventional internal combustion engine (ICE) vehicle, in 2009 the government began to provide generous subsidies for EV purchases. But the price differential and the number of buyers were both large, so paying for the subsidies became extremely costly for the government.

As a result, China’s policymakers planned to phase out the subsidies at the end of 2020 and instead impose a mandate on car manufacturers. Simply stated, the mandate requires that a certain percent of all vehicles sold by a manufacturer each year must be battery-powered. To avoid financial penalties, every year manufacturers must earn a stipulated number of points, which are awarded for each EV produced based on a complex formula that takes into account range, energy efficiency, performance, and more. The requirements get tougher over time, with a goal of having EVs make up 40 percent of all car sales by 2030.

This move will have a huge impact on the worldwide manufacture of EVs, according to William H. Green, the Hoyt C. Hottel Professor in Chemical Engineering. “This is one of the strongest mandates for electric cars worldwide, and it’s being imposed on the largest car market in the world,” he says. “There will be a gigantic increase in the manufacture of EVs and in the production of batteries for them, driving down the cost of both globally.”

But what will be the impact of the mandate within China? The transition to EVs will bring many environmental and other benefits. But how much will it cost the nation? In 2016, MIT chemical engineering colleagues Green and then-graduate student I-Yun Lisa Hsieh PhD ’20 decided to find out. Their goal was to examine the mixed impacts of the mandate on all affected factors: battery prices, manufacturing costs, vehicle prices and sales, and the cost to the consumer of owning and operating a car. Based on their results, they could estimate the total societal cost of complying with the mandate in the coming decade. (Note that the Chinese government recently extended subsidy support for EVs for two years due to the Covid-19 pandemic and that this analysis was performed before that change was announced.)

Looking at battery prices

“The main reason why EVs are costly is that their batteries are expensive,” says Green. In recent years, battery prices have dropped rapidly, largely due to the “learning effect”: As production volumes increase, manufacturers find ways to improve efficiency, and costs go down. It’s generally assumed that battery prices will continue to decrease as EVs take over more of the car market.

Using a new modeling approach, Green and Hsieh determined that learning effects will lower costs appreciably for battery production, but not much for the mining and synthesis of critical battery materials. They concluded that the price of the most widely used EV battery technology — the lithium-ion nickel-manganese-cobalt battery — will indeed drop as more are manufactured. But the decline will slow as the price gets closer to the cost of the raw materials in it.

Using the resulting estimates of battery price, the researchers calculated the extra cost of manufacturing an EV over time and — assuming a standard markup for profit — determined the likely selling price for those cars. In previous work, they had used a variety of data sources and analytical techniques to determine “affordability” for the Chinese population — in other words, the fraction of their income available to spend on buying a car. Based on those findings, they examined the expected impact on car sales in China between 2018 and 2030.

As a baseline for comparison, the researchers first assumed a “counterfactual” (not true-to-life) scenario — car sales without significant adoption of EVs, so without the new mandate. Under that assumption, annual projected car sales climb to more than 34 million by 2030.

When the subsidy on EV purchases is eliminated and the mandate is enacted in 2020, total car sales shrink. But thereafter, the growing economy and rising incomes increase consumer purchasing power and drive up the demand for private car ownership. Annual sales are on average 20 percent lower than in the counterfactual scenario, but they’re projected to reach about 30 million by 2030.

The researchers also projected the breakdown in sales between ICE vehicles and battery EVs at three points in time. According to that analysis, in 2020, EVs make up just 7 percent of the total (1.6 million vehicles). By 2025, that share is up to 21 percent (5.4 million). And by 2030, it’s up to 37 percent (11.2 million) — close to the government’s 40 percent target. Altogether, 66 million EVs are sold between 2020 and 2030.

Those results also track the split between two types of plug-in EVs: pure battery EVs and hybrid EVs (which are powered by both batteries and gasoline). About twice as many pure battery EVs are sold than hybrid EVs, even though the former are more expensive due to the higher cost of their batteries. “The mandate includes a special preference for cars with a longer range, which means cars with large batteries,” says Green. “So carmakers have a big incentive to manufacture the pure battery EVs and be awarded extra points under the mandate formula.”

For the consumer, the added cost of owning an EV includes any difference in vehicle expenses over the whole lifetime of the car. To calculate that difference, the researchers quantified the “total cost of ownership,” or TCO, including the purchase cost, fuel cost, and operating and maintenance costs (including insurance) of their two plug-in EVs and an ICE vehicle out to 2030.

Their results show that before 2020, owning either type of plug-in EV is less costly than owning an ICE vehicle due to the subsidy paid on EV purchases. After the subsidy is removed and the mandate imposed in 2020, owning a hybrid EV is comparable to owning an ICE vehicle. Owning a pure battery EV is more expensive due to its high-cost batteries. Dropping battery prices reduces total ownership cost for both types of EVs, but the pure battery EV remains more expensive out to 2030.

Cost to society

The next step for the researchers was to calculate the total cost to China of forcing the adoption of EVs. The basic approach is straightforward: They take the extra TCO for each EV sold in each year, discount that cost to its present value, and multiply the resulting figure by the number of cars sold in that year. (They exclude taxes embedded in the purchase prices of the vehicle, of electricity and gasoline, and so on, as the society will have to pay other taxes to replace that lost revenue.)

Using that methodology, they calculated the incremental cost to society of each EV sold in each year as well as the extra cost per kilometer driven, assuming that the vehicle has a lifetime of 12 years and is driven 12,500 kilometers each year. The results show that the incremental cost of owning and driving an EV decreases from 2021 to 2030. The cost declines more for pure battery EVs than for hybrid EVs, but the former remain more costly.

By combining the per-car cost to society with the number of cars sold, the researchers calculated the total extra cost incurred. In their results, the total number of EVs sold in a year more than offsets any decrease in per-vehicle cost, so the incremental cost to society grows. And that cost is sizeable. On average, the transition to EVs forced by the mandate will cost 100 billion yuan per year from 2021 to 2030, which is about 2 percent of the nationwide expenditure in the transport sector every year.

During the 10 years from 2021-30, the annual societal cost of the transition to almost 40 percent EVs is equivalent to about 0.1 percent of China’s growing gross domestic product. “So the cost to society of forcing the sale of EVs in place of ICE vehicles is significant,” says Hsieh. “People will have far less money in their pockets to spend on other purchases.”

Other considerations

Green and Hsieh stress that the high societal cost of the forced EV adoption must be considered in light of the potential benefits to be gained. For example, switching from ICE vehicles to EVs will lower air pollution and associated health costs; reduce carbon dioxide emissions to help mitigate climate change; and reduce reliance on imported petroleum, enhancing the country’s national energy security and balance of payments.

Hsieh is now working to quantify those benefits so that the team can perform a proper cost-benefit analysis of China’s transition to EVs. Her initial results suggest that the monetized benefits are — like the costs — substantial. “The benefits appear to be the same order of magnitude as the costs,” she says. “It’s so close that we need to be careful to get the numbers right.”

The researchers cite two other factors that may impact the cost side of the equation. In early 2018, six Chinese megacities with high air pollution began restricting the number of license plates issued for ICE vehicles and charging high fees for them. With their lower-cost, more-abundant “green car plates,” EVs became cost-competitive, and sales soared. To protect Chinese carmakers, the national government recently announced that it plans to end those restrictions. The outcome and its impacts on EV sales remain uncertain. (Again, due to the pandemic, policies restricting car ownership have mostly been relaxed for now.)

The second caveat concerns how carmakers price their vehicles. The results reported here assume that prices are calculated as they are today: the cost of manufacturing the vehicle plus a certain percentage markup for profit. With the new mandate in place, automakers will need to change their pricing strategy so as to persuade enough buyers to purchase EVs to reach the required fraction. “We don’t know what they’re going to do, but one possibility is that they’ll lower the price of their battery cars and raise the price of their gasoline cars,” says Green. “That way, they can still make their profits while operating within the law.” As an example, he cites how U.S. carmakers responded to Corporate Average Fuel Economy standards by adjusting the relative prices of their low- and high-efficiency vehicles.

While such a change in Chinese automakers’ pricing strategy would lower the price of EVs, it would also push up average car prices overall, because the total car sales mix is dominated by ICE vehicles. “Some people in China who would otherwise be able to afford a cheap gasoline car now won’t be able to afford it,” says Hsieh. “They’ll be priced out of the market.”

Green emphasizes the impact of the mandate on all carmakers worldwide. “I can’t overstate how hugely important this is,” he says. “As soon as the mandate came out, carmakers realized that electric vehicles had become a major market rather than a niche market on the side.” And he believes that even without subsidies, the added expense of buying an EV won’t be prohibitive for many car buyers — especially in light of the benefits they offer.

However, he does have a final concern. As more and more EVs are manufactured, global supplies of critical battery materials will become increasingly limited. At the same time, however, the supply of spent batteries will increase, creating an opportunity to recycle critical materials for use in new batteries and simultaneously prevent environmental threats from their disposal. The researchers recommend that policymakers “help to integrate the entire industry chain among automakers, battery producers, used-car dealers, and scrap companies in battery recycling systems to achieve a more sustainable society.”

This research was supported through the MIT Energy Initiative’s Mobility of the Future study.

This article appears in the Autumn 2020 issue of Energy Futures, the magazine of the MIT Energy Initiative. More

Not so long ago, watching a movie on a smartphone seemed impossible. Vivienne Sze was a graduate student at MIT at the time, in the mid 2000s, and she was drawn to the challenge of compressing video to keep image quality high without draining the phone’s battery. The solution she hit upon called for co-designing energy-efficient circuits with energy-efficient algorithms.

Sze would go on to be part of the team that won an Engineering Emmy Award for developing the video compression standards still in use today. Now an associate professor in MIT’s Department of Electrical Engineering and Computer Science, Sze has set her sights on a new milestone: bringing artificial intelligence applications to smartphones and tiny robots.

Her research focuses on designing more-efficient deep neural networks to process video, and more-efficient hardware to run those applications. She recently co-published a book on the topic, and will teach a professional education course on how to design efficient deep learning systems in June.

On April 29, Sze will join Assistant Professor Song Han for an MIT Quest AI Roundtable on the co-design of efficient hardware and software moderated by Aude Oliva, director of MIT Quest Corporate and the MIT director of the MIT-IBM Watson AI Lab. Here, Sze discusses her recent work.

Q: Why do we need low-power AI now?

A: AI applications are moving to smartphones, tiny robots, and internet-connected appliances and other devices with limited power and processing capabilities. The challenge is that AI has high computing requirements. Analyzing sensor and camera data from a self-driving car consumes about 2,500 watts, but the computing budget of a smartphone is just about a single watt. Closing this gap requires rethinking the entire stack, a trend that will define the next decade of AI.

Q: What’s the big deal about running AI on a smartphone?

A: It means that the data processing no longer has to take place in the “cloud,” on racks of warehouse servers. Untethering compute from the cloud allows us to broaden AI’s reach. It gives people in developing countries with limited communication infrastructure access to AI. It also speeds up response time by reducing the lag caused by communicating with distant servers. This is crucial for interactive applications like autonomous navigation and augmented reality, which need to respond instantaneously to changing conditions. Processing data on the device can also protect medical and other sensitive records. Data can be processed right where they’re collected.

Q: What makes modern AI so inefficient?

A: The cornerstone of modern AI — deep neural networks — can require hundreds of millions to billions of calculations — orders of magnitude greater than compressing video on a smartphone. But it’s not just number crunching that makes deep networks energy-intensive — it’s the cost of shuffling data to and from memory to perform these computations. The farther the data have to travel, and the more data there are, the greater the bottleneck.

Q: How are you redesigning AI hardware for greater energy efficiency?

A: We focus on reducing data movement and the amount of data needed for computation. In some deep networks, the same data are used multiple times for different computations. We design specialized hardware to reuse data locally rather than send them off-chip. Storing reused data on-chip makes the process extremely energy-efficient.  

We also optimize the order in which data are processed to maximize their reuse. That’s the key property of the Eyeriss chip that I co-designed with Joel Emer. In our followup work, Eyeriss v2, we made the chip flexible enough to reuse data across a wider range of deep networks. The Eyeriss chip also uses compression to reduce data movement, a common tactic among AI chips. The low-power Navion chip that I co-designed with Sertac Karaman for mapping and navigation applications in robotics uses two to three orders of magnitude less energy than a CPU, in part by using optimizations that reduce the amount of data processed and stored on-chip. 

Q: What changes have you made on the software side to boost efficiency?

A: The more that software aligns with hardware-related performance metrics like energy efficiency, the better we can do. Pruning, for example, is a popular way to remove weights from a deep network to reduce computation costs. But rather than remove weights based on their magnitude, our work on energy-aware pruning suggests you can remove the more energy-intensive weights to improve overall energy consumption. Another method we’ve developed, NetAdapt, automates the process of adapting and optimizing a deep network for a smartphone or other hardware platforms. Our recent followup work, NetAdaptv2, accelerates the optimization process to further boost efficiency.

Q: What low-power AI applications are you working on?

A: I’m exploring autonomous navigation for low-energy robots with Sertac Karaman. I’m also working with Thomas Heldt to develop a low-cost and potentially more effective way of diagnosing and monitoring people with neurodegenerative disorders like Alzheimer’s and Parkinson’s by tracking their eye movements. Eye-movement properties like reaction time could potentially serve as biomarkers for brain function. In the past, eye-movement tracking took place in clinics because of the expensive equipment required. We’ve shown that an ordinary smartphone camera can take measurements from a patient’s home, making data collection easier and less costly. This could help to monitor disease progression and track improvements in clinical drug trials.

Q: Where is low-power AI headed next?

A: Reducing AI’s energy requirements will extend AI to a wider range of embedded devices, extending its reach into tiny robots, smart homes, and medical devices. A key challenge is that efficiency often requires a tradeoff in performance. For wide adoption, it will be important to dig deeper into these different applications to establish the right balance between efficiency and accuracy. More

About a quarter of a percent of the entire gross domestic product of industrialized countries is estimated to be lost through a single technical issue: the fouling of heat exchanger surfaces by salts and other dissolved minerals. This fouling lowers the efficiency of multiple industrial processes and often requires expensive countermeasures such as water pretreatment. Now, findings from MIT could lead to a new way of reducing such fouling, and potentially even enable turning that deleterious process into a productive one that can yield saleable products.

The findings are the result of years of work by recent MIT graduates Samantha McBride PhD ’20 and Henri-Louis Girard PhD ’20 with professor of mechanical engineering Kripa Varanasi. The work, reported today in the journal Science Advances, shows that due to a combination of hydrophobic (water repelling) surfaces and heat, dissolved salts can crystallize in a way that makes it easy to remove them from the surface, in some cases by gravity alone.

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When the researchers began studying the way salts crystallize on such surfaces, they found that the precipitating salt would initially form a partial spherical shell around a droplet. Unexpectedly, this shell would then suddenly rise on a set of spindly leg-like extensions grown during evaporation. The process repeatedly produced  multilegged shapes, resembling elephants and other animals, and even sci-fi droids. The researchers dubbed these formations “crystal critters” in the title of their paper.

After many experiments and detailed analysis, the team determined the mechanism that was producing these leg-like protrusions. They also showed how the protrusions varied depending on temperature and the nature of the hydrophobic surface, which was produced by creating a nanoscale pattern of low ridges. They found that the narrow legs holding up these critter-like forms continue to grow upward from the bottom, as the salty water flows downward through the straw-like legs and precipitates out at the bottom, somewhat like a growing icicle, only balanced on its tip. Eventually the legs become so long they are unable to support the critter’s weight, and the blob of salt crystal breaks off and falls or is swept away.

The work was motivated by the desire to limit or prevent the formation of scaling on surfaces, including inside pipes where such scaling can lead to blockages, Varanasi says. “Samantha’s experiment showed this interesting effect where the scale pretty much just pops off by itself,” he says.

“These legs are hollow tubes, and the liquid is funneled down through these tubes. Once it hits the bottom and evaporates, it forms new crystals that continuously increase the length of the tube,” McBride says. “In the end, you have very, very limited contact between the substrate and the crystal, to the point where these are going to just roll away on their own.”

McBride recalls that in doing the initial experiments as part of her doctoral thesis work, “we definitely suspected that this particular surface would work well for eliminating sodium chloride adhesion, but we didn’t know that a consequence of preventing that adhesion would be the ejection of the entire thing” from the surface.

One key, she found, was the exact scale of the patterns on the surface. While many different length scales of patterning can yield hydrophobic surfaces, only patterns at the nanometer scale achieve this self-ejecting effect. “When you evaporate a drop of salt water on a superhydrophobic surface, usually what happens is those crystals start getting inside of the texture and just form a globe, and they don’t end up lifting off,” McBride says. “So it’s something very specific about the texture and the length scale that we’re looking at here that allows this effect to occur.”

This self-ejecting process, based simply on evaporation from a surface whose texture can be easily produced by etching, abrasion, or coating, could be a boon for a wide variety of processes. All kinds of metal structures in a marine environment or exposed to seawater suffer from scaling and corrosion. The findings may also enable new methods for investigating the mechanisms of scaling and corrosion, the researchers say.

By varying the amount of heat along the surface, it’s even possible to get the crystal formations to roll along in a specific direction, the researchers found. The higher the temperature, the faster the growth and liftoff of these forms takes place, minimizing the amount of time the crystals block the surface.

Heat exchangers are used in a wide variety of different processes, and their efficiency is strongly affected by any surface fouling. Those losses alone, Varanasi says, equal a quarter of a percent of the GDP of the U.S. and other industrialized nations. But fouling is also a major factor in many other areas. It affects pipes in water distribution systems, geothermal wells, agricultural settings, desalination plants, and a variety of renewable energy systems and carbon dioxide conversion methods.

This method, Varanasi says, might even enable the use of untreated salty water in some processes where that would not be practical otherwise, such as in some industrial cooling systems. Further, in some situations the recovered salts and other minerals could be salable products.

While the initial experiments were done with ordinary sodium chloride, other kinds of salts or minerals are expected to produce similar effects, and the researchers are continuing to explore the extension of this process to other kinds of solutions.

Because the methods for making the textures to produce a hydrophobic surface are already well-developed, Varanasi says, implementing this process at large industrial scale should be relatively rapid, and could enable the use of salty or brackish water for cooling systems that would otherwise require the use of valuable and often limited fresh water. For example, in the U.S. alone, a trillion gallons of fresh water are used per year for cooling. A typical 600-megawatt power plant consumes about a billion gallons of water per year, which could be enough to serve 100,000 people. That means that using sea water for cooling where possible could help to alleviate a fresh-water scarcity problem.

“This work shows a remarkable and interesting phenomenon,” says Neelesh Patankar, a professor of mechanical engineering at Northwestern University, who was not associated with this research. The findings, he says, “may lead to an entirely new approach to mitigate mineral fouling in industrial processes. Not only is this work interesting from a fundamental science perspective, in my opinion it is also of practical importance.”

The work was supported by Equinor through MIT Energy Initiative, the MIT Martin Fellowship Program, and the National Science Foundation. More

One of India’s largest commercial and research nuclear reactor facilities lies just south of Arunkumar Seshadri’s hometown of Chennai, India. It was there, during a high school field trip, that the seeds of his interest in nuclear power were planted.

“We learned the basic outline of how a reactor functions,” recalls Seshadri, a fifth-year doctoral student in nuclear science and engineering. “I was fascinated by how such a little bit of uranium or other fuel could produce such an enormous amount of energy.”

This fascination quickly found a formal outlet during Seshadri’s undergraduate years, and continues to propel him now at MIT.

Working closely with his advisor Koroush Shirvan, John Clark Hardwick (1986) Career Development Professor, Seshadri has forged a singular path identifying and testing a new generation of radiation-, corrosion-, and heat-resistant materials and fuels that can better withstand the extreme conditions in nuclear reactors.

His investigations have yielded advances in both basic science and practical technologies, as evidenced by a flurry of journal publications and patents. “I really enjoy answering engineering problems faced by the nuclear industry, and moving toward a more fundamental understanding of the structural and chemical changes” that affect materials in reactors, says Seshadri.

Focused on heat transfer

The son of a Sanskrit language teacher and a stay-at-home mother, Seshadri was dismantling and reassembling his home’s appliances and acquiring programming skills during his elementary years, and competing in science tournaments in high school. At SASTRA University, where he majored in mechanical engineering and controls systems, Seshadri was recruited to a research project funded by the nuclear industry. It proved a decisive experience.

“The goal was to develop sensors for two-phase flow — water and steam — as happens in a boiling water reactor,” he says. “We wanted to extract information about these two different phases so we could learn exactly what was happening inside.” The challenge of designing technology for monitoring and better predicting how heat moves inside a reactor, and the associated prospect of improving operations in commercial nuclear facilities, proved irresistible.  

Inspired by the Indian government’s call for a skilled workforce to help expand its nuclear industry, Seshadri decided to tackle an advanced degree in nuclear science and engineering. “MIT had a rich tradition of experts in two-phase heat transfer; they are torchbearers in the field,” he says. “I thought this could be a place to enrich my interests.”

He found immediate direction for his research pursuits with Shirvan, who was running a series of experiments for the Department of Energy to formulate nuclear reactor fuels with greater accident tolerance. For his master’s thesis, Seshadri examined how different kinds of coating on the metal cladding of nuclear fuels behaved as they were heated up and cooled down. He was particularly interested in wettability, the property that determines whether a material attracts or repels fluids such as the coolants in nuclear reactors.When materials demonstrate higher wettability, coolants can more efficiently carry away heat. But with lower wettability, materials repel fluid, causing steam vapor to form on surfaces, trapping heat and leading to potentially perilous temperature increases within the reactor.

In 2004, Japanese researchers discovered that gamma radiation, a byproduct of the nuclear reactions taking place, enhances wettability. Using high-resolution microscopy, Seshadri helped demonstrate the precise mechanism by which this happens: gamma radiation generated nanosized oxide pores over the surfaces of metal components, creating a highly wettable surface.

“It was the first time anyone showed that gamma radiation created such an impact, and this enabled us to test different coated claddings to improve their heat transfer behaviors,” he says. Seshadri carried out high-resolution microscopic measurements that revealed precisely how gamma radiation affected these coatings.

“I was thrilled,” he says. “To industry, radiation was an evil, damaging surfaces, but we saw that it helped heat transfer.” Some of these enhanced fuel claddings have moved on to industry evaluations. In a twist to their research, Seshadri and Shirvan have patented a technique based on gamma irradiation to make water-repellent coatings, which could be used on windshields or in desalination plants.

Passion for research and teaching

For his doctoral research, Seshadri has shifted to a related area: investigating a silicon carbide composite of great interest to industry as a replacement for a zirconium alloy fuel cladding that is highly susceptible to corrosion. “Silicon carbide composite can withstand very high temperatures and is strong without being brittle,” he says, “but the challenge is that in extreme hydrothermal environments and in the presence of radiation, small quantities of silica dissolve into the coolant, potentially damaging components.”

Seshadri’s job is to determine how different types of reactor radiation contribute to the loss of silicon, and whether that circulating silica is within industry limits. “If silica deposits in reactor components, industry will need to develop a process for removing it,” he says. Seshadri is developing models for predicting the rate of silica dissolution.  His experiments are vital in the search for fuel cladding that can better tolerate temperature fluctuations in the world’s operating light-water reactors. His work will also help in the development of advanced reactors that operate at much higher temperatures, and that use silicon carbide components and molten salt as a coolant.

Seshadri credits Shirvan as integral to his accomplishments and his growth as a researcher. “I came to MIT with no background in nuclear science, and wasn’t doing well,” he recalls. “Shirvan spent countless hours teaching me the essentials I needed, and then giving me the freedom to pursue problems and the time to get results, without pressure.” 

This mode of mentorship powers Seshadri’s own teaching. In his free time, Seshadri supervises undergraduates in India working on energy projects, encouraging them to pursue ambitious goals. On a remote basis, he instructs them in computational modeling, helps them write up research for publication, and apply to graduate schools. “My passions, and all my hobbies, are related to research, and interacting with these students is a step toward the career I want in academia: leading a research team at a university, and getting solutions to deep problems.” More

MIT is committed to driving the transition to a low-carbon world, throwing the full weight of its research forces into transformative technologies for reducing greenhouse gas emissions. But “MIT can’t solve climate change alone,” said Maria T. Zuber, MIT’s vice president for research and the E. A. Griswold Professor of Geophysics, speaking at a virtual symposium in late March.

When MIT initiated its first Climate Action Plan in 2015, a key tenet, said Zuber, was “engagement with actors and entities outside of MIT.” As the Institute prepares to issue an updated version of the plan later this spring, this engagement forum, “Research collaborations to decarbonize the energy system,” was conceived as an opportunity for the MIT community to learn about and comment upon some of the low-carbon research projects between MIT and key outside collaborators. It was co-hosted by the Office of the Vice President for Research and the MIT Energy Initiative (MITEI).

“With vignettes of current or recent engagement activities, we seek to share a small handful of examples of how working with industry has catalyzed progress in the electric power sector, life-cycle analysis to inform decarbonization efforts, and fusion energy, to name a few,” said MITEI Director Robert C. Armstrong, the Chevron Professor of Chemical Engineering, in his introductory remarks.

Symposium speakers, who included MIT faculty and scientists, industry liaisons, and venture capital leaders, made clear that joining forces yields concrete benefits — not simply in specific technologies or sectors, but in the kind of large-scale, market-based solutions required to meet the climate crisis.

Wind, electric vehicles, and nuclear

Take, for instance, the case of Iberdrola, a Spanish-based multinational electric utility with a large renewables portfolio, which is launching a vast fleet of offshore wind farms around the world. As a senior asset performance analysis engineer for the company, Sofia Koukoura found help in modeling the operation of these turbines from Kalyan Veeramachaneni, a principal research scientist with the MIT Laboratory for Information and Decision Systems.

Veeramachaneni harnessed machine learning to predict component failures and likely repairs affecting the longevity of these turbines, providing Koukoura with “flexible, reproducible, and scalable solutions,” she says. “Bridging the gap between development and deployment of a project is a big leap, and the team at MIT is helping us do that.”

Other panels in this session, also moderated by Angela Belcher, the James Mason Crafts Professor of Biological Engineering and Materials Science and Engineering and head of the Department of Biological Engineering, demonstrated the reciprocal nature of MIT’s research with industry associates.

One such case: MITEI research scientist Emre Gençer has developed a life-cycle assessment tool called SESAME (Sustainable Energy Systems Analysis Modeling Environment) to enable a systems-level understanding of the environmental impact and fuel emissions reduction potential of a spectrum of interrelated energy technologies.

ExxonMobil’s Research and Engineering Company — a sponsor of MITEI’s Mobility of the Future Study  — engaged with Gençer to use SESAME for modeling the emissions impacts of switching from internal combustion engine vehicles to hybrid, battery electric, and hydrogen fuel cell vehicles in different regions of the United States. Jennifer Morris, a research scientist with both MITEI and the MIT Joint Program on the Science and Policy of Global Change, provided the various policy scenario projections for the Mobility of the Future Study.

The resulting studies proved useful not just to ExxonMobil, but to the MIT scientists as well.

“In academia, we can come up with solutions, but if they’re not implementable, they’re not as valuable, especially during a climate crisis,” said Gençer. “These connections with industrial sponsors are valuable, because they provide reality checks on our technological and economic assumptions,” said Morris. “These are real-world challenges that make our applications relevant and have real-world impact.” The goal is to make these tools widely available to policymakers, industry, and other stakeholders to inform decision-making that can drive decarbonization.

An example from another research domain: Michael Short the Class of ’42 Associate Professor of Nuclear Science and Engineering (NSE), had been searching for a solution to a vexing, decades-old issue for light water nuclear reactors — the deposition of corrosive deposits on nuclear fuel, which can lead to reactor downtime. 

When Short’s lab cracked this problem of fuel rod fouling, a major U.S. clean energy provider recognized it might be valuable for reducing costs on its nuclear fleet. With support from this company, Short’s lab is now busy developing materials with better resistance to these deposits, which could help keep existing reactors producing clean energy for decades to come.

Beyond such technological advances, Short notes there are less-tangible yet significant rewards to the joint enterprise with industry. When “students have frequent, primary contact with an industry sponsor, they learn they are not just first authors on papers but on patents as well, giving them a sense of what problems they want to work on and what to do with their lives,” he said. If a student solves a problem in science, they will see “someone is ready to snap it up and make an impact on the carbon issue.”

Solar and fusion breakthroughs

In recent years, alliances formed between MIT researchers and outside companies have not merely sparked novel carbon-cutting technologies, but laid the groundwork for path-breaking spinoffs, and even potential new industries. Two panels moderated by Anne White, head of the Department of Nuclear Science and Engineering and the MIT School of Engineering Distinguished Professor of Engineering, featured instructive cases.

When Italian energy company Eni first paired up with MIT in 2008, founding the Solar Frontiers Center (SFC), the initial goal was to “explore everything beyond silicon,” said Massimiliano Pieri, Eni’s cleantech director at Eni Next, Eni’s corporate venture capital organization. After dozens of SFC projects, which have involved a small army of graduate students, generated many patent filings, and produced hundreds of research papers, it is readily apparent that MIT “has dramatically benefited,” said Vladimir Bulović, a professor of electrical engineering and the Fariborz Maseeh Chair in Emerging Technology. Among the results of this mutual venture: a new class of super thin, flexible, and lightweight materials that could vastly expand the use of solar energy.

This long-lived collaboration has also served as the launchpad for such startups as Swift Solar, co-founded by Joel Jean SM ’13, PhD ’17, and Ubiquitous Energy, co-founded by Miles Barr SM ’08, PhD ’12, both of whom earned a Forbes “30 under 30 in Energy” for innovations in the solar industry. Work with Eni at SFC “inspired me to start a career commercializing new solar technology,” said Barr.

In 2016, when researchers in MIT’s Plasma Science and Fusion Center (PSFC) saw a path to making commercial fusion energy a reality, they went big, searching for collaborators who could help “launch a new energy industry,” said Dennis G. Whyte, PSFC director and Hitachi America Professor of Engineering. “It was high risk, but the idea resonated with us,” said Pieri, whose Eni Next firm invested in the MIT spinoff, Commonwealth Fusion Systems (CFS).

With additional investment from Bill Gates’ Breakthrough Energy Ventures and other leading investors in breakthrough energy technologies, said CFS CEO Bob Mumgaard SM ’15, PhD ’15, “We were able to attract talent from all sorts of disciplines much earlier than normally possible, start the company, and scale up quickly.” CFS is now on a fast track to build the world’s first net energy fusion machine, and from there, the first commercially viable fusion power plant, opening a window to limitless clean energy.

By symposium’s end, participants had reached consensus: To achieve the urgent goals of the climate fight, whether by catalyzing new energy industries or deploying cost-effective, carbon-reducing applications, industry and academia must work cooperatively. “We truly need to step up our game — we simply don’t yet have all the technologies we need to decarbonize our energy systems and our economy,” said Zuber. “You’ve heard the phrase, ‘Go big, or go home.’ When it comes to climate change, going big is imperative, because Earth is our home.”   

On April 1, the Office of the Vice President for Research co-hosted another forum, “Viewpoints from the MIT community engaging on climate change: An all-of-MIT approach,” this one in conjunction with the Environmental Solutions Initiative. More

As a small child, Manduhai Buyandelger lived with her grandparents in a house unconnected to the heating grid on the outskirts of Ulaanbaatar, Mongolia. There, in the world’s coldest capital city, temperatures can drop as low as minus 40 degrees Fahrenheit in the winter months.

“Once I moved further into the city with my parents, I had nightmares about my grandparents,” recalls Buyandelger, now a professor of anthropology at MIT. “I felt so vulnerable for them. In the ger district where they lived, most people do not have central heating, and they warm their homes by making fire in their stoves. My grandparents didn’t have heat. I was always worried about them getting up in this icy cold house, carrying buckets of coal from their little shed back into the house, and then using a small shovel putting the coal in the stove. It has been more than 40 years since then, and life there is still very much like that.”

With temperatures this harsh, having access to safe and affordable heat sources is critical for the citizens of Ulaanbaatar, especially for the 60 percent of the population living in the ger district. This suburban area of the city, composed mainly of off-grid nomadic tents, houses some of the city’s poorest and most vulnerable citizens.

Traditionally, the households occupying the ger district kept their homes warm as Buyandelger’s grandparents did, by using individual coal-burning stoves — contributing to Ulaanbaatar’s other “claim to fame” as the world’s most-polluted capital city. In recent years, as air pollution reached levels twice as high as what the World Health Organization defined as “acutely hazardous,” the Mongolian government took measures to combat this pollution. They banned the use of coal in ger district homes and enforced the use of cleaner-burning charcoal briquettes, which in turn created a new set of problems.  

“A lot of people died,” says Buyandelger. “The briquettes are toxic in a different way. Their instructions for burning are nuanced and require more oxygen in the house, which means people have to open their windows and doors, defeating their purpose.” When burned incorrectly, these briquettes generate large amounts of carbon monoxide — an odorless, colorless, and toxic gas.

Establishing interdisciplinary collaborations

Enter Michael Short, the Class of ’42 Associate Professor of Nuclear Science and Engineering (NSE) at MIT. He recognized the need for a safer, cleaner heat source and connected with Buyandelger, whose work in Mongolian anthropology was uniquely suited to aid these efforts. According to Buyandelger, “Oftentimes in history, people adjusted their behaviors so they can use technology. But we can do better and change the technology so that we don’t necessarily jeopardize the people or culture.”

With this goal in mind, Buyandelger, Short, and a team of students from NSE and the Department of Anthropology have begun a collaboration to study the particularities of the local culture, environment, political climate, and economy in Ulaanbaatar to inform their work designing a sustainable, flameless thermal heat source made from molten nitrate salts. Once Covid-19 restrictions have lifted, they plan to travel to Mongolia, where they will live in the ger district with those they aim to help, conducting ethnographic participant observations and extensive interviews to prototype a useful heat bank, observe its functionality in person, and make adaptations and improvements as needed.

For the students, the goal is twofold: They will be trained in “anthropologically informed engineering” and see firsthand the benefits of developing a product with the end-user in mind from the outset; and they will see how targeted, well-informed engineering can empower citizens and in turn preserve democracy.

“Our core hypothesis is that clean fuel independence from the government will foster democratization and prevent setbacks to authoritarianism,” says Buyandelger. She explains that the people in the ger district are heavily dependent on the government: They must agree to use these dangerous fuels or else they will not qualify for other vital government subsidies and food programs. “We want to see if implementing the heat banks would help generate a more open and free society.”

Understanding human complexities

When thinking about climate change and energy challenges across the globe, a lot of emphasis is put on how technology and policy can enact change. But, as illustrated in the Ulaanbaatar project, there is an important, undeniable element that is central: people. 

“For scholars doing this research, if they don’t include the political, social, and cultural dimensions, it is an incomplete project,” says Melissa Nobles. She is the Kenan Sahin Dean of MIT’s School of Humanities, Arts, and Social Sciences (MIT SHASS), as well as a professor of political science.

MIT SHASS is home to 13 academic fields, including anthropology, history, international studies, economics, and music and theater arts — all contributing to understanding the world’s many human complexities. Part of the school’s mission is to generate research and ideas that can change the world for the better, and it helps do this by informing public policy, educating leading science communicators, and shedding light on the cultural barriers that prevent people, organizations, and governments from supporting effective environmental policies and practices.

“Human motivation is hugely complicated,” says Nobles. “The science has been clear on climate change, and it has been clear for a while; but as we see, the facts don’t change people’s behavior. You have to actually get people to ingest it intellectually and emotionally, because part of the resistance is rooted in fears of uncertainty: How am I going to have to change my life? What does it mean for my day-to-day? What does it mean for future generations?”

This question of the day-to-day was something that stuck out to Buyandelger when thinking about the cultural and social challenges their heat bank might face: “How do we distribute this? How heavy is it; will people be able to carry it? Who in the household will receive it? Can the temperature be altered for cooking?”

Integrating climate into curriculum

In MIT’s SHASS classrooms, students learn to think critically about these big sociopolitical questions through some 30 courses that tackle climate and energy topics. Presented through rigorous humanities and social science lenses, the subjects range from history to literature to economics to political science to philosophy.Courses include 24.07 (The Ethics of Climate Change), a moral philosophy class in which students explore the ethical implications of a rapidly warming world; CMS.375 (Reading Climate through Media), in which students learn how contemporary media shapes public perceptions about climate issues, as well as how to craft effective climate stories and messages themselves; and 21H.421 (Introduction to Environmental History), which explores the influence of planetary life and conditions on human history, and the reciprocal influence of people on the Earth.

Clare Balboni, the 3M Career Development Assistant Professor of Environmental Economics, teaches graduate- and undergraduate-level courses on environmental policy and economics. The undergraduate-level course, which will be taught this semester for the first time in several years, fulfills an elective requirement for MIT’s energy studies minor. Balboni joined the Department of Economics in 2019 and has been working toward making environmental economics a core topic in the department.

“It’s a really exciting time in environmental economics, and there is a tremendous amount of interest from the student body,” Balboni says. “There is a longstanding tradition of theoretical work in this area, but more recently there has also been an upsurge in related empirical work. This reflects in part increased awareness and political and policy focus on environmental issues, but also enormous opportunities presented by new data sources, which make it possible to study environmental phenomena in ways that we weren’t previously able to do.” 

She explains that economic studies can be key to informing effective climate solutions. “Understanding economic incentives and human behavior and responses is crucial. For instance, pollutants and climate damages can affect a wide range of human outcomes, such as mortality and health, labor productivity, education, conflict, and crime, which it is critical to understand and quantify when thinking about environmental policy design and implementation.”

A growing area of interest for MIT’s School of Humanities, Arts, and Social Sciences is how to continue incorporating climate into its curriculum across all of its varied academic disciplines. As climate change issues become an even more important topic in national legislation and policymaking — especially with the new Biden-Harris administration in office — Nobles expects research and teaching to follow suit.

She explains that “what literature does, what music does, what art can do, what studying philosophy, culture, politics, and economics can do, is help students understand why it’s so complicated for climate change efforts to move forward, and then, what they can do to help.” More

The long-term goals of the Paris Agreement — keeping global warming well below 2 degrees Celsius and ideally 1.5 C in order to avert the worst impacts of climate change — may not be achievable by greenhouse gas emissions-reduction measures alone. Most scenarios for meeting these targets also require the deployment of negative emissions technologies (NETs) that remove carbon dioxide (CO2) from the atmosphere.

A leading NET candidate is bioenergy with carbon capture and storage (BECCS), which extracts energy from CO2-absorbing plants, captures CO2 that’s released into the atmosphere when the extracted plant matter is combusted, and stores it underground. The end-to-end process entails securing available land, cultivating and transporting crops, converting biomass into electricity with carbon capture, and transporting and storing the captured CO2.

On first glance, it may seem like a no-brainer to ramp up BECCS technology around the world to ensure that the international effort to stabilize the climate will succeed. But the prospect of cultivating plants for BECCS on a massive scale has raised concerns about adverse, unintended consequences. These include environmental impacts that range from soil erosion to biodiversity loss, and economic impacts, especially higher food prices that could result from redirecting vast tracts of agricultural land to draw down carbon emissions.

A new study in the journal Global Environmental Change focuses squarely on the economic implications of BECCS. Representing all major components of BECCS in the MIT Economic Projection and Policy Analysis (EPPA) model, researchers at the MIT Joint Program on the Science and Policy of Global Change and Imperial College London estimate the likely impacts of the technology on the global economy under climate policy scenarios that keep global warming below 1.5 C and 2 C, respectively.

They find that while it’s economically feasible to implement such policies without relying on BECCS, large-scale deployment of the technology in the second half of the century significantly lowers the overall implementation costs. Moreover, the inclusion of BECCS in these policies prevents widespread economic damages: in the 1.5 C scenario, global consumption decreases by almost 20 percent by 2100 without BECCS, but only by 5 percent with BECCS.

“Our modeling suggests that the benefits of BECCS far outweigh the costs,” says Howard Herzog, senior research engineer at the MIT Energy Initiative and co-author of the study. “In terms of costs, BECCS fares better than direct air capture, the other major negative emissions technology that uses carbon dioxide capture and storage (CCS).”

BECCS also significantly reduces the carbon prices associated with cap-and-trade policies designed to reduce emissions sufficiently to keep global warming below 1.5 C and 2 C. By creating negative emissions, the technology relieves pressure from the emissions cap and therefore lowers the price of emissions permits. At the same time, BECCS is compensated for its negative emissions through the carbon price, which is a substantial source of revenue.

“We conduct a series of experiments which robustly demonstrate that revenue from carbon permits is really driving the deployment of BECCS,” says Jennifer Morris, study co-author and research scientist at the MIT Joint Program and MIT Energy Initiative. “We find that the value of CO2 removal is far greater than the value of the electricity generation. Electricity is essentially a byproduct.”

Finally, the study concludes that while BECCS deployment results in major changes in land use to accommodate bioenergy crop cultivation consistent with meeting the 1.5 C and 2 C climate targets, it drives up the prices of food, livestock, and crops by less than 5 percent on average by 2100 (up to 15 percent in selected regions). Most notably, food prices rise by just 1.5 percent globally.

These results suggest that, in concert with dramatic emissions-reduction measures, BECCS could be an economically effective tool in the global effort to stabilize the climate.

“We have shown that large-scale deployment of BECCS could dramatically lower the costs of implementing policies aimed at meeting the long-term climate goals of the Paris Agreement, and avoid major price increases in agricultural commodities,” says MIT Joint Program deputy director and MITEI Senior Research Scientist Sergey Paltsev, who co-authored the study. “Further research is needed, however, to provide a more granular assessment of food supply chains and BECCS components, and to ensure that such deployment is politically viable.” More

In a September 2020 essay in Nature Energy, three scientists posed several “grand challenges” — one of which was to find suitable materials for thermal energy storage devices that could be used in concert with solar energy systems. Fortuitously, Mingda Li — the Norman C. Rasmussen Assistant Professor of Nuclear Science and Engineering at MIT, who heads the department’s Quantum Matter Group — was already thinking along similar lines. In fact, Li and nine collaborators (from MIT, Lawrence Berkeley National Laboratory, and Argonne National Laboratory) were developing a new methodology, involving a novel machine-learning approach, that would make it faster and easier to identify materials with favorable properties for thermal energy storage and other uses.

The results of their investigation appear this month in a paper for Advanced Science. “This is a revolutionary approach that promises to accelerate the design of new functional materials,” comments physicist Jaime Fernandez-Baca, a distinguished staff member at Oak Ridge National Laboratory.

A central challenge in materials science, Li and his coauthors write, is to “establish structure-property relationships” — to figure out the characteristics a material with a given atomic structure would have. Li’s team focused, in particular, on using structural knowledge to predict the “phonon density of states,” which has a critical bearing on thermal properties.

To understand that term, it’s best to start with the word phonon. “A crystalline material is composed of atoms arranged in a lattice structure,” explains Nina Andrejevic, a PhD student in materials science and engineering. “We can think of these atoms as spheres connected by springs, and thermal energy causes the springs to vibrate. And those vibrations, which only occur at discrete [quantized] frequencies or energies, are what we call phonons.”

The phonon density of states is simply the number of vibrational modes, or phonons, found within a given frequency or energy range. Knowing the phonon density of states, one can determine a material’s heat-carrying capacity as well as its thermal conductivity, which relates to how readily heat passes through a material, and even the superconducting transition temperature in a superconductor. “For thermal energy storage purposes, you want a material with a high specific heat, which means it can take in heat without a sharp rise in temperature,” Li says. “You also want a material with low thermal conductivity so that it retains its heat longer.”

The phonon density of states, however, is a difficult term to measure experimentally or to compute theoretically. “For a measurement like this, one has to go to a national laboratory to use a large instrument, about 10 meters long, in order to get the energy resolution you need,” Li says. “That’s because the signal we’re looking for is very weak.”

“And if you want to calculate the phonon density of states, the most accurate way of doing so relies on density functional perturbation theory (DFPT),” notes Zhantao Chen, a mechanical engineering PhD student. “But those calculations scale with the fourth order of the number of atoms in the crystal’s basic building block, which could require days of computing time on a CPU cluster.” For alloys, which contain two or more elements, the calculations become much harder, possibly taking weeks or even longer.

The new method, says Li, could reduce those computational demands to a few seconds on a PC. Rather than trying to calculate the phonon density of states from first principles, which is clearly a laborious task, his team employed a neural network approach, utilizing artificial intelligence algorithms that enable a computer to learn from example. The idea was to present the neural network with enough data on a material’s atomic structure and its associated phonon density of states that the network could discern the key patterns connecting the two. After “training” in this fashion, the network would hopefully make reliable density of states predictions for a substance with a given atomic structure.

Predictions are difficult, Li explains, because the phonon density of states cannot by described by a single number but rather by a curve (analogous to the spectrum of light given off at different wavelengths by a luminous object). “Another challenge is that we only have trustworthy [density of states] data for about 1,500 materials. When we first tried machine learning, the dataset was too small to support accurate predictions.”

His group then teamed up with Lawrence Berkeley physicist Tess Smidt ’12, a co-inventor of so-called Euclidean neural networks. “Training a conventional neural network normally requires datasets containing hundreds of thousands to millions of examples,” Smidt says. A significant part of that data demand stems from the fact that a conventional neural network does not understand that a 3D pattern and a rotated version of the same pattern are related and actually represent the same thing. Before it can recognize 3D patterns — in this case, the precise geometric arrangement of atoms in a crystal — a conventional neural network first needs to be shown the same pattern in hundreds of different orientations.

“Because Euclidean neural networks understand geometry — and recognize that rotated patterns still ‘mean’ the same thing — they can extract the maximal amount of information from a single sample,” Smidt adds. As a result, a Euclidean neural network trained on 1,500 examples can outperform a conventional neural network trained on 500 times more data.

Using the Euclidean neural network, the team predicted phonon density of states for 4,346 crystalline structures. They then selected the materials with the 20 highest heat capacities, comparing the predicted density of states values with those obtained through time-consuming DFPT calculations. The agreement was remarkably close.

The approach can be used to pick out promising thermal energy storage materials, in keeping with the aforementioned “grand challenge,” Li says. “But it could also greatly facilitate alloy design, because we can now determine the density of states for alloys just as easily as for crystals. That, in turn, offers a huge expansion in possible materials we could consider for thermal storage, as well as many other applications.”

Some applications have, in fact, already begun. Computer code from the MIT group has been installed on machines at Oak Ridge, enabling researchers to predict the phonon density of states of a given material based on its atomic structure.

Andrejevic points out, moreover, that Euclidean neural networks have even broader potential that is as-of-yet untapped. “They can help us figure out important material properties besides the phonon density of states. So this could open up the field in a big way.”

This research was funded by the U.S. Department of Energy Office of Science, National Science Foundation, and Lawrence Berkeley National Laboratory. More