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    Turning up the heat on next-generation semiconductors

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    The scorching surface of Venus, where temperatures can climb to 480 degrees Celsius (hot enough to melt lead), is an inhospitable place for humans and machines alike. One reason scientists have not yet been able to send a rover to the planet’s surface is because silicon-based electronics can’t operate in such extreme temperatures for an extended period of time.For high-temperature applications like Venus exploration, researchers have recently turned to gallium nitride, a unique material that can withstand temperatures of 500 degrees or more.The material is already used in some terrestrial electronics, like phone chargers and cell phone towers, but scientists don’t have a good grasp of how gallium nitride devices would behave at temperatures beyond 300 degrees, which is the operational limit of conventional silicon electronics.In a new paper published in Applied Physics Letters, which is part of a multiyear research effort, a team of scientists from MIT and elsewhere sought to answer key questions about the material’s properties and performance at extremely high temperatures.  They studied the impact of temperature on the ohmic contacts in a gallium nitride device. Ohmic contacts are key components that connect a semiconductor device with the outside world.The researchers found that extreme temperatures didn’t cause significant degradation to the gallium nitride material or contacts. They were surprised to see that the contacts remained structurally intact even when held at 500 degrees Celsius for 48 hours.Understanding how contacts perform at extreme temperatures is an important step toward the group’s next goal of developing high-performance transistors that could operate on the surface of Venus. Such transistors could also be used on Earth in electronics for applications like extracting geothermal energy or monitoring the inside of jet engines.“Transistors are the heart of most modern electronics, but we didn’t want to jump straight to making a gallium nitride transistor because so much could go wrong. We first wanted to make sure the material and contacts could survive, and figure out how much they change as you increase the temperature. We’ll design our transistor from these basic material building blocks,” says John Niroula, an electrical engineering and computer science (EECS) graduate student and lead author of the paper.His co-authors include Qingyun Xie PhD ’24; Mengyang Yuan PhD ’22; EECS graduate students Patrick K. Darmawi-Iskandar and Pradyot Yadav; Gillian K. Micale, a graduate student in the Department of Materials Science and Engineering; senior author Tomás Palacios, the Clarence J. LeBel Professor of EECS, director of the Microsystems Technology Laboratories, and a member of the Research Laboratory of Electronics; as well as collaborators Nitul S. Rajput of the Technology Innovation Institute of the United Arab Emirates; Siddharth Rajan of Ohio State University; Yuji Zhao of Rice University; and Nadim Chowdhury of Bangladesh University of Engineering and Technology.Turning up the heatWhile gallium nitride has recently attracted much attention, the material is still decades behind silicon when it comes to scientists’ understanding of how its properties change under different conditions. One such property is resistance, the flow of electrical current through a material.A device’s overall resistance is inversely proportional to its size. But devices like semiconductors have contacts that connect them to other electronics. Contact resistance, which is caused by these electrical connections, remains fixed no matter the size of the device. Too much contact resistance can lead to higher power dissipation and slower operating frequencies for electronic circuits.“Especially when you go to smaller dimensions, a device’s performance often ends up being limited by contact resistance. People have a relatively good understanding of contact resistance at room temperature, but no one has really studied what happens when you go all the way up to 500 degrees,” Niroula says.For their study, the researchers used facilities at MIT.nano to build gallium nitride devices known as transfer length method structures, which are composed of a series of resistors. These devices enable them to measure the resistance of both the material and the contacts.They added ohmic contacts to these devices using the two most common methods. The first involves depositing metal onto gallium nitride and heating it to 825 degrees Celsius for about 30 seconds, a process called annealing.The second method involves removing chunks of gallium nitride and using a high-temperature technology to regrow highly doped gallium nitride in its place, a process led by Rajan and his team at Ohio State. The highly doped material contains extra electrons that can contribute to current conduction.“The regrowth method typically leads to lower contact resistance at room temperature, but we wanted to see if these methods still work well at high temperatures,” Niroula says.A comprehensive approachThey tested devices in two ways. Their collaborators at Rice University, led by Zhao, conducted short-term tests by placing devices on a hot chuck that reached 500 degrees Celsius and taking immediate resistance measurements.At MIT, they conducted longer-term experiments by placing devices into a specialized furnace the group previously developed. They left devices inside for up to 72 hours to measure how resistance changes as a function of temperature and time.Microscopy experts at MIT.nano (Aubrey N. Penn) and the Technology Innovation Institute (Nitul S. Rajput) used state-of-the-art transmission electron microscopes to see how such high temperatures affect gallium nitride and the ohmic contacts at the atomic level.“We went in thinking the contacts or the gallium nitride material itself would degrade significantly, but we found the opposite. Contacts made with both methods seemed to be remarkably stable,” says Niroula.While it is difficult to measure resistance at such high temperatures, their results indicate that contact resistance seems to remain constant even at temperatures of 500 degrees, for around 48 hours. And just like at room temperature, the regrowth process led to better performance.The material did start to degrade after being in the furnace for 48 hours, but the researchers are already working to boost long-term performance. One strategy involves adding protective insulators to keep the material from being directly exposed to the high-temperature environment.Moving forward, the researchers plan to use what they learned in these experiments to develop high-temperature gallium nitride transistors.“In our group, we focus on innovative, device-level research to advance the frontiers of microelectronics, while adopting a systematic approach across the hierarchy, from the material level to the circuit level. Here, we have gone all the way down to the material level to understand things in depth. In other words, we have translated device-level advancements to circuit-level impact for high-temperature electronics, through design, modeling and complex fabrication. We are also immensely fortunate to have forged close partnerships with our longtime collaborators in this journey,” Xie says.This work was funded, in part, by the U.S. Air Force Office of Scientific Research, Lockheed Martin Corporation, the Semiconductor Research Corporation through the U.S. Defense Advanced Research Projects Agency, the U.S. Department of Energy, Intel Corporation, and the Bangladesh University of Engineering and Technology.Fabrication and microscopy were conducted at MIT.nano, the Semiconductor Epitaxy and Analysis Laboratory at Ohio State University, the Center for Advanced Materials Characterization at the University of Oregon, and the Technology Innovation Institute of the United Arab Emirates. More

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      From steel engineering to ovarian tumor research

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      Ashutosh Kumar is a classically trained materials engineer. Having grown up with a passion for making things, he has explored steel design and studied stress fractures in alloys.Throughout Kumar’s education, however, he was also drawn to biology and medicine. When he was accepted into an undergraduate metallurgical engineering and materials science program at Indian Institute of Technology (IIT) Bombay, the native of Jamshedpur was very excited — and “a little dissatisfied, since I couldn’t do biology anymore.”Now a PhD candidate and a MathWorks Fellow in MIT’s Department of Materials Science and Engineering, Kumar can merge his wide-ranging interests. He studies the effect of certain bacteria that have been observed encouraging the spread of ovarian cancer and possibly reducing the effectiveness of chemotherapy and immunotherapy.“Some microbes have an affinity toward infecting ovarian cancer cells, which can lead to changes in the cellular structure and reprogramming cells to survive in stressful conditions,” Kumar says. “This means that cells can migrate to different sites and may have a mechanism to develop chemoresistance. This opens an avenue to develop therapies to see if we can start to undo some of these changes.”Kumar’s research combines microbiology, bioengineering, artificial intelligence, big data, and materials science. Using microbiome sequencing and AI, he aims to define microbiome changes that may correlate with poor patient outcomes. Ultimately, his goal is to engineer bacteriophage viruses to reprogram bacteria to work therapeutically.Kumar started inching toward work in the health sciences just months into earning his bachelor’s degree at IIT Bombay.“I realized engineering is so flexible that its applications extend to any field,” he says, adding that he started working with biomaterials “to respect both my degree program and my interests.”“I loved it so much that I decided to go to graduate school,” he adds.Starting his PhD program at MIT, he says, “was a fantastic opportunity to switch gears and work on more interdisciplinary or ‘MIT-type’ work.”Kumar says he and Angela Belcher, the James Mason Crafts Professor of biological engineering and materials science, began discussing the impact of the microbiome on ovarian cancer when he first arrived at MIT.“I shared my enthusiasm about human health and biology, and we started brainstorming,” he says. “We realized that there’s an unmet need to understand a lot of gynecological cancers. Ovarian cancer is an aggressive cancer, which is usually diagnosed when it’s too late and has already spread.”In 2022, Kumar was awarded a MathWorks Fellowship. The fellowships are awarded to School of Engineering graduate students, preferably those who use MATLAB or Simulink — which were developed by the mathematical computer software company MathWorks — in their research. The philanthropic support fueled Kumar’s full transition into health science research.“The work we are doing now was initially not funded by traditional sources, and the MathWorks Fellowship gave us the flexibility to pursue this field,” Kumar says. “It provided me with opportunities to learn new skills and ask questions about this topic. MathWorks gave me a chance to explore my interests and helped me navigate from being a steel engineer to a cancer scientist.”Kumar’s work on the relationship between bacteria and ovarian cancer started with studying which bacteria are incorporated into tumors in mouse models.“We started looking closely at changes in cell structure and how those changes impact cancer progression,” he says, adding that MATLAB image processing helps him and his collaborators track tumor metastasis.The research team also uses RNA sequencing and MATLAB algorithms to construct a taxonomy of the bacteria.“Once we have identified the microbiome composition,” Kumar says, “we want to see how the microbiome changes as cancer progresses and identify changes in, let’s say, patients who develop chemoresistance.”He says recent findings that ovarian cancer may originate in the fallopian tubes are promising because detecting cancer-related biomarkers or lesions before cancer spreads to the ovaries could lead to better prognoses.As he pursues his research, Kumar says he is extremely thankful to Belcher “for believing in me to work on this project.“She trusted me and my passion for making an impact on human health — even though I come from a materials engineering background — and supported me throughout. It was her passion to take on new challenges that made it possible for me to work on this idea. She has been an amazing mentor and motivated me to continue moving forward.”For her part, Belcher is equally enthralled.“It has been amazing to work with Ashutosh on this ovarian cancer microbiome project,” she says. “He has been so passionate and dedicated to looking for less-conventional approaches to solve this debilitating disease. His innovations around looking for very early changes in the microenvironment of this disease could be critical in interception and prevention of ovarian cancer. We started this project with very little preliminary data, so his MathWorks fellowship was critical in the initiation of the project.”Kumar, who has been very active in student government and community-building activities, believes it is very important for students to feel included and at home at their institutions so they can develop in ways outside of academics. He says that his own involvement helps him take time off from work.“Science can never stop, and there will always be something to do,” he says, explaining that he deliberately schedules time off and that social engagement helps him to experience downtime. “Engaging with community members through events on campus or at the dorm helps set a mental boundary with work.”Regarding his unusual route through materials science to cancer research, Kumar regards it as something that occurred organically.“I have observed that life is very dynamic,” he says. “What we think we might do versus what we end up doing is never consistent. Five years back, I had no idea I would be at MIT working with such excellent scientific mentors around me.” More

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      This 3D printer can figure out how to print with an unknown material

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      While 3D printing has exploded in popularity, many of the plastic materials these printers use to create objects cannot be easily recycled. While new sustainable materials are emerging for use in 3D printing, they remain difficult to adopt because 3D printer settings need to be adjusted for each material, a process generally done by hand.

      To print a new material from scratch, one must typically set up to 100 parameters in software that controls how the printer will extrude the material as it fabricates an object. Commonly used materials, like mass-manufactured polymers, have established sets of parameters that were perfected through tedious, trial-and-error processes.

      But the properties of renewable and recyclable materials can fluctuate widely based on their composition, so fixed parameter sets are nearly impossible to create. In this case, users must come up with all these parameters by hand.

      Researchers tackled this problem by developing a 3D printer that can automatically identify the parameters of an unknown material on its own.

      A collaborative team from MIT’s Center for Bits and Atoms (CBA), the U.S. National Institute of Standards and Technology (NIST), and the National Center for Scientific Research in Greece (Demokritos) modified the extruder, the “heart” of a 3D printer, so it can measure the forces and flow of a material.

      These data, gathered through a 20-minute test, are fed into a mathematical function that is used to automatically generate printing parameters. These parameters can be entered into off-the-shelf 3D printing software and used to print with a never-before-seen material. 

      The automatically generated parameters can replace about half of the parameters that typically must be tuned by hand. In a series of test prints with unique materials, including several renewable materials, the researchers showed that their method can consistently produce viable parameters.

      This research could help to reduce the environmental impact of additive manufacturing, which typically relies on nonrecyclable polymers and resins derived from fossil fuels.

      “In this paper, we demonstrate a method that can take all these interesting materials that are bio-based and made from various sustainable sources and show that the printer can figure out by itself how to print those materials. The goal is to make 3D printing more sustainable,” says senior author Neil Gershenfeld, who leads CBA.

      His co-authors include first author Jake Read a graduate student in the CBA who led the printer development; Jonathan Seppala, a chemical engineer in the Materials Science and Engineering Division of NIST; Filippos Tourlomousis, a former CBA postdoc who now heads the Autonomous Science Lab at Demokritos; James Warren, who leads the Materials Genome Program at NIST; and Nicole Bakker, a research assistant at CBA. The research is published in the journal Integrating Materials and Manufacturing Innovation.

      Shifting material properties

      In fused filament fabrication (FFF), which is often used in rapid prototyping, molten polymers are extruded through a heated nozzle layer-by-layer to build a part. Software, called a slicer, provides instructions to the machine, but the slicer must be configured to work with a particular material.

      Using renewable or recycled materials in an FFF 3D printer is especially challenging because there are so many variables that affect the material properties.

      For instance, a bio-based polymer or resin might be composed of different mixes of plants based on the season. The properties of recycled materials also vary widely based on what is available to recycle.

      “In ‘Back to the Future,’ there is a ‘Mr. Fusion’ blender where Doc just throws whatever he has into the blender and it works [as a power source for the DeLorean time machine]. That is the same idea here. Ideally, with plastics recycling, you could just shred what you have and print with it. But, with current feed-forward systems, that won’t work because if your filament changes significantly during the print, everything would break,” Read says.

      To overcome these challenges, the researchers developed a 3D printer and workflow to automatically identify viable process parameters for any unknown material.

      They started with a 3D printer their lab had previously developed that can capture data and provide feedback as it operates. The researchers added three instruments to the machine’s extruder that take measurements which are used to calculate parameters.

      A load cell measures the pressure being exerted on the printing filament, while a feed rate sensor measures the thickness of the filament and the actual rate at which it is being fed through the printer.

      “This fusion of measurement, modeling, and manufacturing is at the heart of the collaboration between NIST and CBA, as we work develop what we’ve termed ‘computational metrology,’” says Warren.

      These measurements can be used to calculate the two most important, yet difficult to determine, printing parameters: flow rate and temperature. Nearly half of all print settings in standard software are related to these two parameters. 

      Deriving a dataset

      Once they had the new instruments in place, the researchers developed a 20-minute test that generates a series of temperature and pressure readings at different flow rates. Essentially, the test involves setting the print nozzle at its hottest temperature, flowing the material through at a fixed rate, and then turning the heater off.

      “It was really difficult to figure out how to make that test work. Trying to find the limits of the extruder means that you are going to break the extruder pretty often while you are testing it. The notion of turning the heater off and just passively taking measurements was the ‘aha’ moment,” says Read.

      These data are entered into a function that automatically generates real parameters for the material and machine configuration, based on relative temperature and pressure inputs. The user can then enter those parameters into 3D printing software and generate instructions for the printer.

      In experiments with six different materials, several of which were bio-based, the method automatically generated viable parameters that consistently led to successful prints of a complex object.

      Moving forward, the researchers plan to integrate this process with 3D printing software so parameters don’t need to be entered manually. In addition, they want to enhance their workflow by incorporating a thermodynamic model of the hot end, which is the part of the printer that melts the filament.

      This collaboration is now more broadly developing computational metrology, in which the output of a measurement is a predictive model rather than just a parameter. The researchers will be applying this in other areas of advanced manufacturing, as well as in expanding access to metrology.

      “By developing a new method for the automatic generation of process parameters for fused filament fabrication, this study opens the door to the use of recycled and bio-based filaments that have variable and unknown behaviors. Importantly, this enhances the potential for digital manufacturing technology to utilize locally sourced sustainable materials,” says Alysia Garmulewicz, an associate professor in the Faculty of Administration and Economics at the University of Santiago in Chile who was not involved with this work.

      This research is supported, in part, by the National Institute of Standards and Technology and the Center for Bits and Atoms Consortia. More

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      Power when the sun doesn’t shine

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      In 2016, at the huge Houston energy conference CERAWeek, MIT materials scientist Yet-Ming Chiang found himself talking to a Tesla executive about a thorny problem: how to store the output of solar panels and wind turbines for long durations.        

      Chiang, the Kyocera Professor of Materials Science and Engineering, and Mateo Jaramillo, a vice president at Tesla, knew that utilities lacked a cost-effective way to store renewable energy to cover peak levels of demand and to bridge the gaps during windless and cloudy days. They also knew that the scarcity of raw materials used in conventional energy storage devices needed to be addressed if renewables were ever going to displace fossil fuels on the grid at scale.

      Energy storage technologies can facilitate access to renewable energy sources, boost the stability and reliability of power grids, and ultimately accelerate grid decarbonization. The global market for these systems — essentially large batteries — is expected to grow tremendously in the coming years. A study by the nonprofit LDES (Long Duration Energy Storage) Council pegs the long-duration energy storage market at between 80 and 140 terawatt-hours by 2040. “That’s a really big number,” Chiang notes. “Every 10 people on the planet will need access to the equivalent of one EV [electric vehicle] battery to support their energy needs.”

      In 2017, one year after they met in Houston, Chiang and Jaramillo joined forces to co-found Form Energy in Somerville, Massachusetts, with MIT graduates Marco Ferrara SM ’06, PhD ’08 and William Woodford PhD ’13, and energy storage veteran Ted Wiley.

      “There is a burgeoning market for electrical energy storage because we want to achieve decarbonization as fast and as cost-effectively as possible,” says Ferrara, Form’s senior vice president in charge of software and analytics.

      Investors agreed. Over the next six years, Form Energy would raise more than $800 million in venture capital.

      Bridging gaps

      The simplest battery consists of an anode, a cathode, and an electrolyte. During discharge, with the help of the electrolyte, electrons flow from the negative anode to the positive cathode. During charge, external voltage reverses the process. The anode becomes the positive terminal, the cathode becomes the negative terminal, and electrons move back to where they started. Materials used for the anode, cathode, and electrolyte determine the battery’s weight, power, and cost “entitlement,” which is the total cost at the component level.

      During the 1980s and 1990s, the use of lithium revolutionized batteries, making them smaller, lighter, and able to hold a charge for longer. The storage devices Form Energy has devised are rechargeable batteries based on iron, which has several advantages over lithium. A big one is cost.

      Chiang once declared to the MIT Club of Northern California, “I love lithium-ion.” Two of the four MIT spinoffs Chiang founded center on innovative lithium-ion batteries. But at hundreds of dollars a kilowatt-hour (kWh) and with a storage capacity typically measured in hours, lithium-ion was ill-suited for the use he now had in mind.

      The approach Chiang envisioned had to be cost-effective enough to boost the attractiveness of renewables. Making solar and wind energy reliable enough for millions of customers meant storing it long enough to fill the gaps created by extreme weather conditions, grid outages, and when there is a lull in the wind or a few days of clouds.

      To be competitive with legacy power plants, Chiang’s method had to come in at around $20 per kilowatt-hour of stored energy — one-tenth the cost of lithium-ion battery storage.

      But how to transition from expensive batteries that store and discharge over a couple of hours to some as-yet-undefined, cheap, longer-duration technology?

      “One big ball of iron”

      That’s where Ferrara comes in. Ferrara has a PhD in nuclear engineering from MIT and a PhD in electrical engineering and computer science from the University of L’Aquila in his native Italy. In 2017, as a research affiliate at the MIT Department of Materials Science and Engineering, he worked with Chiang to model the grid’s need to manage renewables’ intermittency.

      How intermittent depends on where you are. In the United States, for instance, there’s the windy Great Plains; the sun-drenched, relatively low-wind deserts of Arizona, New Mexico, and Nevada; and the often-cloudy Pacific Northwest.

      Ferrara, in collaboration with Professor Jessika Trancik of MIT’s Institute for Data, Systems, and Society and her MIT team, modeled four representative locations in the United States and concluded that energy storage with capacity costs below roughly $20/kWh and discharge durations of multiple days would allow a wind-solar mix to provide cost-competitive, firm electricity in resource-abundant locations.

      Now that they had a time frame, they turned their attention to materials. At the price point Form Energy was aiming for, lithium was out of the question. Chiang looked at plentiful and cheap sulfur. But a sulfur, sodium, water, and air battery had technical challenges.

      Thomas Edison once used iron as an electrode, and iron-air batteries were first studied in the 1960s. They were too heavy to make good transportation batteries. But this time, Chiang and team were looking at a battery that sat on the ground, so weight didn’t matter. Their priorities were cost and availability.

      “Iron is produced, mined, and processed on every continent,” Chiang says. “The Earth is one big ball of iron. We wouldn’t ever have to worry about even the most ambitious projections of how much storage that the world might use by mid-century.” If Form ever moves into the residential market, “it’ll be the safest battery you’ve ever parked at your house,” Chiang laughs. “Just iron, air, and water.”

      Scientists call it reversible rusting. While discharging, the battery takes in oxygen and converts iron to rust. Applying an electrical current converts the rusty pellets back to iron, and the battery “breathes out” oxygen as it charges. “In chemical terms, you have iron, and it becomes iron hydroxide,” Chiang says. “That means electrons were extracted. You get those electrons to go through the external circuit, and now you have a battery.”

      Form Energy’s battery modules are approximately the size of a washer-and-dryer unit. They are stacked in 40-foot containers, and several containers are electrically connected with power conversion systems to build storage plants that can cover several acres.

      The right place at the right time

      The modules don’t look or act like anything utilities have contracted for before.

      That’s one of Form’s key challenges. “There is not widespread knowledge of needing these new tools for decarbonized grids,” Ferrara says. “That’s not the way utilities have typically planned. They’re looking at all the tools in the toolkit that exist today, which may not contemplate a multi-day energy storage asset.”

      Form Energy’s customers are largely traditional power companies seeking to expand their portfolios of renewable electricity. Some are in the process of decommissioning coal plants and shifting to renewables.

      Ferrara’s research pinpointing the need for very low-cost multi-day storage provides key data for power suppliers seeking to determine the most cost-effective way to integrate more renewable energy.

      Using the same modeling techniques, Ferrara and team show potential customers how the technology fits in with their existing system, how it competes with other technologies, and how, in some cases, it can operate synergistically with other storage technologies.

      “They may need a portfolio of storage technologies to fully balance renewables on different timescales of intermittency,” he says. But other than the technology developed at Form, “there isn’t much out there, certainly not within the cost entitlement of what we’re bringing to market.”  Thanks to Chiang and Jaramillo’s chance encounter in Houston, Form has a several-year lead on other companies working to address this challenge. 

      In June 2023, Form Energy closed its biggest deal to date for a single project: Georgia Power’s order for a 15-megawatt/1,500-megawatt-hour system. That order brings Form’s total amount of energy storage under contracts with utility customers to 40 megawatts/4 gigawatt-hours. To meet the demand, Form is building a new commercial-scale battery manufacturing facility in West Virginia.

      The fact that Form Energy is creating jobs in an area that lost more than 10,000 steel jobs over the past decade is not lost on Chiang. “And these new jobs are in clean tech. It’s super exciting to me personally to be doing something that benefits communities outside of our traditional technology centers.

      “This is the right time for so many reasons,” Chiang says. He says he and his Form Energy co-founders feel “tremendous urgency to get these batteries out into the world.”

      This article appears in the Winter 2024 issue of Energy Futures, the magazine of the MIT Energy Initiative. More

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      On the hunt for sustainable materials

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      By the time she started high school, Avni Singhal had attended six different schools in a variety of settings, from a traditional public school to a self-paced program. The transitions opened her eyes to how widely educational environments can vary, and made her think about that impact on students.

      “Experiencing so many different types of educational systems exposed me to different ways of looking at things and how that shapes people’s worldviews,” says Singhal.

      Now a fourth-year PhD student in the Department of Materials Science and Engineering, Singhal is still thinking about increasing opportunities for her fellow students, while also pursuing her research. She devotes herself to both developing sustainable materials and improving the graduate experience in her department.

      She recently completed her two-year term as a student representative on the department’s graduate studies committee. In this role, she helped revamp the communication around the qualifying exams and introducing student input to the faculty search process.

      “It’s given me a lot of insight into how our department works,” says Singhal. “It’s a chance to get to know faculty, bring up issues that students experience, and work on changing things that we think could be improved.”

      At the same time, Singhal uses atomistic simulations to model material properties, with an eye toward sustainability. She is a part of the Learning Matter Lab, a group that merges data science tools with engineering and physics-based simulation to better design and understand materials. As part of a computational group, Singhal has worked on a range of projects in collaboration with other labs that are looking to combine computing with other disciplines. Some of this work is sponsored by the MIT Climate and Sustainability Consortium, which facilitates connections across MIT labs and industry.

      Joining the Learning Matter Lab was a step out of Singhal’s comfort zone. She arrived at MIT from the University of California at Berkeley with a joint degree in materials science and bioengineering, as well as a degree in electrical engineering and computer science.

      “I was generally interested in doing work on environment-related applications,” says Singhal. “I was pretty hesitant at first to switch entirely to computation because it’s a very different type of lifestyle of research than what I was doing before.”

      Singhal has taken the challenge in stride, contributing to projects including improving carbon capture molecules and developing new deconstructable, degradable plastics. Not only does Singhal have to understand the technical details of her own work, she also needs to understand the big picture and how to best wield the expertise of her collaborators.

      “When I came in, I was very wide-eyed, thinking computation can do everything because I had never done it before,” says Singhal. “It’s that curve where you know a little bit about something, and you think it can do everything. And then as you learn more, you learn where it can and can’t help us, where it can be valuable, and how to figure out in what part of a project it’s useful.”

      Singhal applies a similarly critical lens when thinking about graduate school as a whole. She notes that access to information and resources is often the main factor determining who enters selective educational programs, and that such access becomes increasingly limited at the graduate level.

      “I realized just how much applying is a function of knowing how to do it,” says Singhal, who co-organized and volunteers with the DMSE Application Assistance Program. The program matches prospective applicants with current students to give feedback on their application materials and provide insight into what it’s like attending MIT. Some of the first students Singhal mentored through the program are now also participants as well.

      “The further you get in your educational career, the more you realize how much assistance you got along the way to get where you are,” says Singhal. “That happens at every stage.”

      Looking toward the future, Singhal wants to continue to pursue research with a sustainability impact. She also wants to continue mentoring in some capacity but isn’t in a rush to figure out exactly what that will look like.

      “Grad school doesn’t mean I have to do one thing. I can stay open to all the possibilities of what comes next.”  More

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      Learning the language of molecules to predict their properties

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      Discovering new materials and drugs typically involves a manual, trial-and-error process that can take decades and cost millions of dollars. To streamline this process, scientists often use machine learning to predict molecular properties and narrow down the molecules they need to synthesize and test in the lab.

      Researchers from MIT and the MIT-Watson AI Lab have developed a new, unified framework that can simultaneously predict molecular properties and generate new molecules much more efficiently than these popular deep-learning approaches.

      To teach a machine-learning model to predict a molecule’s biological or mechanical properties, researchers must show it millions of labeled molecular structures — a process known as training. Due to the expense of discovering molecules and the challenges of hand-labeling millions of structures, large training datasets are often hard to come by, which limits the effectiveness of machine-learning approaches.

      By contrast, the system created by the MIT researchers can effectively predict molecular properties using only a small amount of data. Their system has an underlying understanding of the rules that dictate how building blocks combine to produce valid molecules. These rules capture the similarities between molecular structures, which helps the system generate new molecules and predict their properties in a data-efficient manner.

      This method outperformed other machine-learning approaches on both small and large datasets, and was able to accurately predict molecular properties and generate viable molecules when given a dataset with fewer than 100 samples.

      “Our goal with this project is to use some data-driven methods to speed up the discovery of new molecules, so you can train a model to do the prediction without all of these cost-heavy experiments,” says lead author Minghao Guo, a computer science and electrical engineering (EECS) graduate student.

      Guo’s co-authors include MIT-IBM Watson AI Lab research staff members Veronika Thost, Payel Das, and Jie Chen; recent MIT graduates Samuel Song ’23 and Adithya Balachandran ’23; and senior author Wojciech Matusik, a professor of electrical engineering and computer science and a member of the MIT-IBM Watson AI Lab, who leads the Computational Design and Fabrication Group within the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). The research will be presented at the International Conference for Machine Learning.

      Learning the language of molecules

      To achieve the best results with machine-learning models, scientists need training datasets with millions of molecules that have similar properties to those they hope to discover. In reality, these domain-specific datasets are usually very small. So, researchers use models that have been pretrained on large datasets of general molecules, which they apply to a much smaller, targeted dataset. However, because these models haven’t acquired much domain-specific knowledge, they tend to perform poorly.

      The MIT team took a different approach. They created a machine-learning system that automatically learns the “language” of molecules — what is known as a molecular grammar — using only a small, domain-specific dataset. It uses this grammar to construct viable molecules and predict their properties.

      In language theory, one generates words, sentences, or paragraphs based on a set of grammar rules. You can think of a molecular grammar the same way. It is a set of production rules that dictate how to generate molecules or polymers by combining atoms and substructures.

      Just like a language grammar, which can generate a plethora of sentences using the same rules, one molecular grammar can represent a vast number of molecules. Molecules with similar structures use the same grammar production rules, and the system learns to understand these similarities.

      Since structurally similar molecules often have similar properties, the system uses its underlying knowledge of molecular similarity to predict properties of new molecules more efficiently. 

      “Once we have this grammar as a representation for all the different molecules, we can use it to boost the process of property prediction,” Guo says.

      The system learns the production rules for a molecular grammar using reinforcement learning — a trial-and-error process where the model is rewarded for behavior that gets it closer to achieving a goal.

      But because there could be billions of ways to combine atoms and substructures, the process to learn grammar production rules would be too computationally expensive for anything but the tiniest dataset.

      The researchers decoupled the molecular grammar into two parts. The first part, called a metagrammar, is a general, widely applicable grammar they design manually and give the system at the outset. Then it only needs to learn a much smaller, molecule-specific grammar from the domain dataset. This hierarchical approach speeds up the learning process.

      Big results, small datasets

      In experiments, the researchers’ new system simultaneously generated viable molecules and polymers, and predicted their properties more accurately than several popular machine-learning approaches, even when the domain-specific datasets had only a few hundred samples. Some other methods also required a costly pretraining step that the new system avoids.

      The technique was especially effective at predicting physical properties of polymers, such as the glass transition temperature, which is the temperature required for a material to transition from solid to liquid. Obtaining this information manually is often extremely costly because the experiments require extremely high temperatures and pressures.

      To push their approach further, the researchers cut one training set down by more than half — to just 94 samples. Their model still achieved results that were on par with methods trained using the entire dataset.

      “This grammar-based representation is very powerful. And because the grammar itself is a very general representation, it can be deployed to different kinds of graph-form data. We are trying to identify other applications beyond chemistry or material science,” Guo says.

      In the future, they also want to extend their current molecular grammar to include the 3D geometry of molecules and polymers, which is key to understanding the interactions between polymer chains. They are also developing an interface that would show a user the learned grammar production rules and solicit feedback to correct rules that may be wrong, boosting the accuracy of the system.

      This work is funded, in part, by the MIT-IBM Watson AI Lab and its member company, Evonik. More

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      Is it topological? A new materials database has the answer

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      What will it take to make our electronics smarter, faster, and more resilient? One idea is to build them from materials that are topological.

      Topology stems from a branch of mathematics that studies shapes that can be manipulated or deformed without losing certain core properties. A donut is a common example: If it were made of rubber, a donut could be twisted and squeezed into a completely new shape, such as a coffee mug, while retaining a key trait — namely, its center hole, which takes the form of the cup’s handle. The hole, in this case, is a topological trait, robust against certain deformations.

      In recent years, scientists have applied concepts of topology to the discovery of materials with similarly robust electronic properties. In 2007, researchers predicted the first electronic topological insulators — materials in which electrons that behave in ways that are “topologically protected,” or persistent in the face of certain disruptions.

      Since then, scientists have searched for more topological materials with the aim of building better, more robust electronic devices. Until recently, only a handful of such materials were identified, and were therefore assumed to be a rarity.

      Now researchers at MIT and elsewhere have discovered that, in fact, topological materials are everywhere, if you know how to look for them.

      In a paper published today in Science, the team, led by Nicolas Regnault of Princeton University and the École Normale Supérieure Paris, reports harnessing the power of multiple supercomputers to map the electronic structure of more than 96,000 natural and synthetic crystalline materials. They applied sophisticated filters to determine whether and what kind of topological traits exist in each structure.

      Overall, they found that 90 percent of all known crystalline structures contain at least one topological property, and more than 50 percent of all naturally occurring materials exhibit some sort of topological behavior.

      “We found there’s a ubiquity — topology is everywhere,” says Benjamin Wieder, the study’s co-lead, and a postdoc in MIT’s Department of Physics.

      The team has compiled the newly identified materials into a new, freely accessible Topological Materials Database resembling a periodic table of topology. With this new library, scientists can quickly search materials of interest for any topological properties they might hold, and harness them to build ultra-low-power transistors, new magnetic memory storage, and other devices with robust electronic properties.

      The paper includes co-lead author Maia Vergniory of the Donostia International Physics Center, Luis Elcoro of the University of Basque Country, Stuart Parkin and Claudia Felser of the Max Planck Institute, and Andrei Bernevig of Princeton University.

      Beyond intuition

      The new study was motivated by a desire to speed up the traditional search for topological materials.

      “The way the original materials were found was through chemical intuition,” Wieder says. “That approach had a lot of early successes. But as we theoretically predicted more kinds of topological phases, it seemed intuition wasn’t getting us very far.”

      Wieder and his colleagues instead utilized an efficient and systematic method to root out signs of topology, or robust electronic behavior, in all known crystalline structures, also known as inorganic solid-state materials.

      For their study, the researchers looked to the Inorganic Crystal Structure Database, or ICSD, a repository into which researchers enter the atomic and chemical structures of crystalline materials that they have studied. The database includes materials found in nature, as well as those that have been synthesized and manipulated in the lab. The ICSD is currently the largest materials database in the world, containing over 193,000 crystals whose structures have been mapped and characterized.

      The team downloaded the entire ICSD, and after performing some data cleaning to weed out structures with corrupted files or incomplete data, the researchers were left with just over 96,000 processable structures. For each of these structures, they performed a set of calculations based on fundamental knowledge of the relation between chemical constituents, to produce a map of the material’s electronic structure, also known as the electron band structure.

      The team was able to efficiently carry out the complicated calculations for each structure using multiple supercomputers, which they then employed to perform a second set of operations, this time to screen for various known topological phases, or persistent electrical behavior in each crystal material.

      “We’re looking for signatures in the electronic structure in which certain robust phenomena should occur in this material,” explains Wieder, whose previous work involved refining and expanding the screening technique, known as topological quantum chemistry.

      From their high-throughput analysis, the team quickly discovered a surprisingly large number of materials that are naturally topological, without any experimental manipulation, as well as materials that can be manipulated, for instance with light or chemical doping, to exhibit some sort of robust electronic behavior. They also discovered a handful of materials that contained more than one topological state when exposed to certain conditions.

      “Topological phases of matter in 3D solid-state materials have been proposed as venues for observing and manipulating exotic effects, including the interconversion of electrical current and electron spin, the tabletop simulation of exotic theories from high-energy physics, and even, under the right conditions, the storage and manipulation of quantum information,” Wieder notes. 

      For experimentalists who are studying such effects, Wieder says the team’s new database now reveals a menagerie of new materials to explore.

      This research was funded, in part, by the U.S. Department of Energy, the National Science Foundation, and the Office of Naval Research. More

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      Generating new molecules with graph grammar

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      Chemical engineers and materials scientists are constantly looking for the next revolutionary material, chemical, and drug. The rise of machine-learning approaches is expediting the discovery process, which could otherwise take years. “Ideally, the goal is to train a machine-learning model on a few existing chemical samples and then allow it to produce as many manufacturable molecules of the same class as possible, with predictable physical properties,” says Wojciech Matusik, professor of electrical engineering and computer science at MIT. “If you have all these components, you can build new molecules with optimal properties, and you also know how to synthesize them. That’s the overall vision that people in that space want to achieve”

      However, current techniques, mainly deep learning, require extensive datasets for training models, and many class-specific chemical datasets contain a handful of example compounds, limiting their ability to generalize and generate physical molecules that could be created in the real world.

      Now, a new paper from researchers at MIT and IBM tackles this problem using a generative graph model to build new synthesizable molecules within the same chemical class as their training data. To do this, they treat the formation of atoms and chemical bonds as a graph and develop a graph grammar — a linguistics analogy of systems and structures for word ordering — that contains a sequence of rules for building molecules, such as monomers and polymers. Using the grammar and production rules that were inferred from the training set, the model can not only reverse engineer its examples, but can create new compounds in a systematic and data-efficient way. “We basically built a language for creating molecules,” says Matusik “This grammar essentially is the generative model.”

      Matusik’s co-authors include MIT graduate students Minghao Guo, who is the lead author, and Beichen Li as well as Veronika Thost, Payal Das, and Jie Chen, research staff members with IBM Research. Matusik, Thost, and Chen are affiliated with the MIT-IBM Watson AI Lab. Their method, which they’ve called data-efficient graph grammar (DEG), will be presented at the International Conference on Learning Representations.

      “We want to use this grammar representation for monomer and polymer generation, because this grammar is explainable and expressive,” says Guo. “With only a few number of the production rules, we can generate many kinds of structures.”

      A molecular structure can be thought of as a symbolic representation in a graph — a string of atoms (nodes) joined together by chemical bonds (edges). In this method, the researchers allow the model to take the chemical structure and collapse a substructure of the molecule down to one node; this may be two atoms connected by a bond, a short sequence of bonded atoms, or a ring of atoms. This is done repeatedly, creating the production rules as it goes, until a single node remains. The rules and grammar then could be applied in the reverse order to recreate the training set from scratch or combined in different combinations to produce new molecules of the same chemical class.

      “Existing graph generation methods would produce one node or one edge sequentially at a time, but we are looking at higher-level structures and, specifically, exploiting chemistry knowledge, so that we don’t treat the individual atoms and bonds as the unit. This simplifies the generation process and also makes it more data-efficient to learn,” says Chen.

      Further, the researchers optimized the technique so that the bottom-up grammar was relatively simple and straightforward, such that it fabricated molecules that could be made.

      “If we switch the order of applying these production rules, we would get another molecule; what’s more, we can enumerate all the possibilities and generate tons of them,” says Chen. “Some of these molecules are valid and some of them not, so the learning of the grammar itself is actually to figure out a minimal collection of production rules, such that the percentage of molecules that can actually be synthesized is maximized.” While the researchers concentrated on three training sets of less than 33 samples each — acrylates, chain extenders, and isocyanates — they note that the process could be applied to any chemical class.

      To see how their method performed, the researchers tested DEG against other state-of-the-art models and techniques, looking at percentages of chemically valid and unique molecules, diversity of those created, success rate of retrosynthesis, and percentage of molecules belonging to the training data’s monomer class.

      “We clearly show that, for the synthesizability and membership, our algorithm outperforms all the existing methods by a very large margin, while it’s comparable for some other widely-used metrics,” says Guo. Further, “what is amazing about our algorithm is that we only need about 0.15 percent of the original dataset to achieve very similar results compared to state-of-the-art approaches that train on tens of thousands of samples. Our algorithm can specifically handle the problem of data sparsity.”

      In the immediate future, the team plans to address scaling up this grammar learning process to be able to generate large graphs, as well as produce and identify chemicals with desired properties.

      Down the road, the researchers see many applications for the DEG method, as it’s adaptable beyond generating new chemical structures, the team points out. A graph is a very flexible representation, and many entities can be symbolized in this form — robots, vehicles, buildings, and electronic circuits, for example. “Essentially, our goal is to build up our grammar, so that our graphic representation can be widely used across many different domains,” says Guo, as “DEG can automate the design of novel entities and structures,” says Chen.

      This research was supported, in part, by the MIT-IBM Watson AI Lab and Evonik. More

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      Computational modeling guides development of new materials

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      Metal-organic frameworks, a class of materials with porous molecular structures, have a variety of possible applications, such as capturing harmful gases and catalyzing chemical reactions. Made of metal atoms linked by organic molecules, they can be configured in hundreds of thousands of different ways.

      To help researchers sift through all of the possible metal-organic framework (MOF) structures and help identify the ones that would be most practical for a particular application, a team of MIT computational chemists has developed a model that can analyze the features of a MOF structure and predict if it will be stable enough to be useful.

      The researchers hope that these computational predictions will help cut the development time of new MOFs.

      “This will allow researchers to test the promise of specific materials before they go through the trouble of synthesizing them,” says Heather Kulik, an associate professor of chemical engineering at MIT.

      The MIT team is now working to develop MOFs that could be used to capture methane gas and convert it to useful compounds such as fuels.

      The researchers described their new model in two papers, one in the Journal of the American Chemical Society and one in Scientific Data. Graduate students Aditya Nandy and Gianmarco Terrones are the lead authors of the Scientific Data paper, and Nandy is also the lead author of the JACS paper. Kulik is the senior author of both papers.

      Modeling structure

      MOFs consist of metal atoms joined by organic molecules called linkers to create a rigid, cage-like structure. The materials also have many pores, which makes them useful for catalyzing reactions involving gases but can also make them less structurally stable.

      “The limitation in seeing MOFs realized at industrial scale is that although we can control their properties by controlling where each atom is in the structure, they’re not necessarily that stable, as far as materials go,” Kulik says. “They’re very porous and they can degrade under realistic conditions that we need for catalysis.”

      Scientists have been working on designing MOFs for more than 20 years, and thousands of possible structures have been published. A centralized repository contains about 10,000 of these structures but is not linked to any of the published findings on the properties of those structures.

      Kulik, who specializes in using computational modeling to discover structure-property relationships of materials, wanted to take a more systematic approach to analyzing and classifying the properties of MOFs.

      “When people make these now, it’s mostly trial and error. The MOF dataset is really promising because there are so many people excited about MOFs, so there’s so much to learn from what everyone’s been working on, but at the same time, it’s very noisy and it’s not systematic the way it’s reported,” she says.

      Kulik and her colleagues set out to analyze published reports of MOF structures and properties using a natural-language-processing algorithm. Using this algorithm, they scoured nearly 4,000 published papers, extracting information on the temperature at which a given MOF would break down. They also pulled out data on whether particular MOFs can withstand the conditions needed to remove solvents used to synthesize them and make sure they become porous.

      Once the researchers had this information, they used it to train two neural networks to predict MOFs’ thermal stability and stability during solvent removal, based on the molecules’ structure.

      “Before you start working with a material and thinking about scaling it up for different applications, you want to know will it hold up, or is it going to degrade in the conditions I would want to use it in?” Kulik says. “Our goal was to get better at predicting what makes a stable MOF.”

      Better stability

      Using the model, the researchers were able to identify certain features that influence stability. In general, simpler linkers with fewer chemical groups attached to them are more stable. Pore size is also important: Before the researchers did their analysis, it had been thought that MOFs with larger pores might be too unstable. However, the MIT team found that large-pore MOFs can be stable if other aspects of their structure counteract the large pore size.

      “Since MOFs have so many things that can vary at the same time, such as the metal, the linkers, the connectivity, and the pore size, it is difficult to nail down what governs stability across different families of MOFs,” Nandy says. “Our models enable researchers to make predictions on existing or new materials, many of which have yet to be made.”

      The researchers have made their data and models available online. Scientists interested in using the models can get recommendations for strategies to make an existing MOF more stable, and they can also add their own data and feedback on the predictions of the models.

      The MIT team is now using the model to try to identify MOFs that could be used to catalyze the conversion of methane gas to methanol, which could be used as fuel. Kulik also plans to use the model to create a new dataset of hypothetical MOFs that haven’t been built before but are predicted to have high stability. Researchers could then screen this dataset for a variety of properties.

      “People are interested in MOFs for things like quantum sensing and quantum computing, all sorts of different applications where you need metals distributed in this atomically precise way,” Kulik says.

      The research was funded by DARPA, the U.S. Office of Naval Research, the U.S. Department of Energy, a National Science Foundation Graduate Research Fellowship, a Career Award at the Scientific Interface from the Burroughs Wellcome Fund, and an AAAS Marion Milligan Mason Award. More