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Machine-learning tool could help develop tougher materials | MIT News

For engineers developing new materials or protective coatings, there are billions of different possibilities to sort through. Lab tests or even detailed computer simulations to determine their exact properties, such as toughness, can take hours, days, or more for each variation. Now, a new artificial intelligence-based approach developed at MIT could reduce that to a matter of milliseconds, making it practical to screen vast arrays of candidate materials.

The system, which MIT researchers hope could be used to develop stronger protective coatings or structural materials — for example, to protect aircraft or spacecraft from impacts — is described in a paper in the journal Matter, by MIT postdoc Chi-Hua Yu, civil and environmental engineering professor and department head Markus J. Buehler, and Yu-Chuan Hsu at the National Taiwan University.

The focus of this work was on predicting the way a material would break or fracture, by analyzing the propagation of cracks through the material’s molecular structure. Buehler and his colleagues have spent many years studying fractures and other failure modes in great detail, since understanding failure processes is key to developing robust, reliable materials. “One of the specialties of my lab is to use what we call molecular dynamics simulations, or basically atom-by-atom simulations” of such processes, Buehler says.

These simulations provide a chemically accurate description of how fracturing happens, he says. But it’s slow, because it requires solving equations of motion for every single atom. “It takes a lot of time to simulate these processes,” he says. The team decided to explore ways of streamlining that process, using a machine-learning system.

“We’re kind of taking a detour,” he says. “We’ve been asking, what if you had just the observation of how fracturing happens [in a given material], and let computers learn this relationship itself?” To do that, artificial intelligence (AI) systems need a variety of examples to use as a training set, to learn about the correlations between the material’s characteristics and its performance.

In this case, they were looking at a variety of composite, layered coatings made of crystalline materials. The variables included the composition of the layers and the relative orientations of their orderly crystal structures, and the way those materials each responded to fracturing, based on the molecular dynamics simulations. “We basically simulate, atom by atom, how materials break, and we record that information,” Buehler says.

The team used atom-by-atom simulations to determine how cracks propagate through different materials. This animation shows one such simulation, in which the crack propagates all the way through.

They painstakingly generated hundreds of such simulations, with a wide variety of structures, and subjected each one to many different simulated fractures. Then they fed large amounts of data about all these simulations into their AI system, to see if it could discover the underlying physical principles and predict the performance of a new material that was not part of the training set.

And it did. “That’s the really exciting thing,” Buehler says, “because the computer simulation through AI can do what normally takes a very long time using molecular dynamics, or using finite element simulations, which are another way that engineers solve this problem, and it’s very slow as well. So, this is a whole new way of simulating how materials fail.”

How materials fail is crucial information for any engineering project, Buehler emphasizes. Materials failures such as fractures are “one of the biggest reasons for losses in any industry. For inspecting planes or trains or cars, or for roads or infrastructure, or concrete, or steel corrosion, or to understand the fracture of biological tissues such as bone, the ability to simulate fracturing with AI, and doing that quickly and very efficiently, is a real game changer.”

The improvement in speed produced by using this method is remarkable. Hsu explains that “for single simulations in molecular dynamics, it has taken several hours to run the simulations, but in this artificial intelligence prediction, it only takes 10 milliseconds to go through all the predictions from the patterns, and show how a crack forms step by step.”

“Over the past 30 years or so there have been multiple approaches to model crack propagation in solids, but it remains a formidable and computationally expensive problem,” says Pradeep Guduru, a professor of engineering at Brown University, who was not involved in this work. “By shifting the computational expense to training a robust machine-learning algorithm, this new approach can potentially result in a quick and computationally inexpensive design tool, which is always desirable for practical applications.”

The method they developed is quite generalizable, Buehler says. “Even though in our paper we only applied it to one material with different crystal orientations, you can apply this methodology to much more complex materials.” And while they used data from atomistic simulations, the system could also be used to make predictions on the basis of experimental data such as images of a material undergoing fracturing.

“If we had a new material that we’ve never simulated before,” he says, “if we have a lot of images of the fracturing process, we can feed that data into the machine-learning model as well.” Whatever the input, simulated or experimental, the AI system essentially goes through the evolving process frame by frame, noting how each image differs from the one before in order to learn the underlying dynamics.

For example, as researchers make use of the new facilities in MIT.nano, the Institute’s facility dedicated to fabricating and testing materials at the nanoscale, vast amounts of new data about a variety of synthesized materials will be generated.

“As we have more and more high-throughput experimental techniques that can produce a lot of images very quickly, in an automated way, these kind of data sources can immediately be fed into the machine-learning model,” Buehler says. “We really think that the future will be one where we have a lot more integration between experiment and simulation, much more than we have in the past.”

The system could be applied not just to fracturing, as the team did in this initial demonstration, but to a wide variety of processes unfolding over time, he says, such as diffusion of one material into another, or corrosion processes. “Anytime where you have evolutions of physical fields, and we want to know how these fields evolve as a function of the microstructure,” he says, this method could be a boon.

The research was supported by the U.S. Office of Naval Research and the Army Research Office.

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Undergraduates develop next-generation intelligence tools | MIT News

The coronavirus pandemic has driven us apart physically while reminding us of the power of technology to connect. When MIT shut its doors in March, much of campus moved online, to virtual classes, labs, and chatrooms. Among those making the pivot were students engaged in independent research under MIT’s Undergraduate Research Opportunities Program (UROP). 

With regular check-ins with their advisors via Slack and Zoom, many students succeeded in pushing through to the end. One even carried on his experiments from his bedroom, after schlepping his Sphero Bolt robots home in a backpack. “I’ve been so impressed by their resilience and dedication,” says Katherine Gallagher, one of three artificial intelligence engineers at MIT Quest for Intelligence who works with students each semester on intelligence-related applications. “There was that initial week of craziness and then they were right back to work.” Four projects from this spring are highlighted below.

Learning to explore the world with open eyes and ears

Robots rely heavily on images beamed through their built-in cameras, or surrogate “eyes,” to get around. MIT senior Alon Kosowsky-Sachs thinks they could do a lot more if they also used their microphone “ears.” 

From his home in Sharon, Massachusetts, where he retreated after MIT closed in March, Kosowsky-Sachs is training four baseball-sized Sphero Bolt robots to roll around a homemade arena. His goal is to teach the robots to pair sights with sounds, and to exploit this information to build better representations of their environment. He’s working with Pulkit Agrawal, an assistant professor in MIT’s Department of Electrical Engineering and Computer Science, who is interested in designing algorithms with human-like curiosity.

While Kosowsky-Sachs sleeps, his robots putter away, gliding through an object-strewn rink he built for them from two-by-fours. Each burst of movement becomes a pair of one-second video and audio clips. By day, Kosowsky-Sachs trains a “curiosity” model aimed at pushing the robots to become bolder, and more skillful, at navigating their obstacle course.

“I want them to see something through their camera, and hear something from their microphone, and know that these two things happen together,” he says. “As humans, we combine a lot of sensory information to get added insight about the world. If we hear a thunder clap, we don’t need to see lightning to know that a storm has arrived. Our hypothesis is that robots with a better model of the world will be able to accomplish more difficult tasks.”

Training a robot agent to design a more efficient nuclear reactor 

One important factor driving the cost of nuclear power is the layout of its reactor core. If fuel rods are arranged in an optimal fashion, reactions last longer, burn less fuel, and need less maintenance. As engineers look for ways to bring down the cost of nuclear energy, they are eying the redesign of the reactor core.

“Nuclear power emits very little carbon and is surprisingly safe compared to other energy sources, even solar or wind,” says third-year student Isaac Wolverton. “We wanted to see if we could use AI to make it more efficient.” 

In a project with Josh Joseph, an AI engineer at the MIT Quest, and Koroush Shirvan, an assistant professor in MIT’s Department of Nuclear Science and Engineering, Wolverton spent the year training a reinforcement learning agent to find the best way to lay out fuel rods in a reactor core. To simulate the process, he turned the problem into a game, borrowing a machine learning technique for producing agents with superhuman abilities at chess and Go.

He started by training his agent on a simpler problem: arranging colored tiles on a grid so that as few tiles as possible of the same color would touch. As Wolverton increased the number of options, from two colors to five, and four tiles to 225, he grew excited as the agent continued to find the best strategy. “It gave us hope we could teach it to swap the cores into an optimal arrangement,” he says.

Eventually, Wolverton moved to an environment meant to simulate a 36-rod reactor core, with two enrichment levels and 2.1 million possible core configurations. With input from researchers in Shirvan’s lab, Wolverton trained an agent that arrived at the optimal solution.

The lab is now building on Wolverton’s code to try to train an agent in a life-sized 100-rod environment with 19 enrichment levels. “There’s no breakthrough at this point,” he says. “But we think it’s possible, if we can find enough compute resources.”

Making more livers available to patients who need them

About 8,000 patients in the United States receive liver transplants each year, but that’s only half the number who need one. Many more livers might be made available if hospitals had a faster way to screen them, researchers say. In a collaboration with Massachusetts General Hospital, MIT Quest is evaluating whether automation could help to boost the nation’s supply of viable livers.  

In approving a liver for transplant, pathologists estimate its fat content from a slice of tissue. If it’s low enough, the liver is deemed ready for transplant. But there are often not enough qualified doctors to review tissue samples on the tight timeline needed to match livers with recipients. A shortage of doctors, coupled with the subjective nature of analyzing tissue, means that viable livers are inevitably discarded.

This loss represents a huge opportunity for machine learning, says third-year student Kuan Wei Huang, who joined the project to explore AI applications in health care. The project involves training a deep neural network to pick out globules of fat on liver tissue slides to estimate the liver’s overall fat content.

One challenge, says Huang, has been figuring out how to handle variations in how various pathologists classify fat globules. “This makes it harder to tell whether I’ve created the appropriate masks to feed into the neural net,” he says. “However, after meeting with experts in the field, I received clarifications and was able to continue working.”

Trained on images labeled by pathologists, the model will eventually learn to isolate fat globules in unlabeled images on its own. The final output will be a fat content estimate with pictures of highlighted fat globules showing how the model arrived at its final count. “That’s the easy part — we just count up the pixels in the highlighted globules as a percentage of the overall biopsy and we have our fat content estimate,” says the Quest’s Gallagher, who is leading the project.

Huang says he’s excited by the project’s potential to help people. “Using machine learning to address medical problems is one of the best ways that a computer scientist can impact the world.”

Exposing the hidden constraints of what we mean in what we say

Language shapes our understanding of the world in subtle ways, with slight variations in the words we use conveying sharply different meanings. The sentence, “Elephants live in Africa and Asia,” looks a lot like the sentence “Elephants eat twigs and leaves.” But most readers will conclude that the elephants in the first sentence are split into distinct groups living on separate continents but not apply the same reasoning to the second sentence, because eating twigs and eating leaves can both be true of the same elephant in a way that living on different continents cannot.

Karen Gu is a senior majoring in computer science and molecular biology, but instead of putting cells under a microscope for her SuperUROP project, she chose to look at sentences like the ones above. “I’m fascinated by the complex and subtle things that we do to constrain language understanding, almost all of it subconsciously,” she says.

Working with Roger Levy, a professor in MIT’s Department of Brain and Cognitive Sciences, and postdoc MH Tessler, Gu explored how prior knowledge guides our interpretation of syntax and ultimately, meaning. In the sentences above, prior knowledge about geography and mutual exclusivity interact with syntax to produce different meanings.

After steeping herself in linguistics theory, Gu built a model to explain how, word by word, a given sentence produces meaning. She then ran a set of online experiments to see how human subjects would interpret analogous sentences in a story. Her experiments, she says, largely validated intuitions from linguistic theory.

One challenge, she says, was having to reconcile two approaches for studying language. “I had to figure out how to combine formal linguistics, which applies an almost mathematical approach to understanding how words combine, and probabilistic semantics-pragmatics, which has focused more on how people interpret whole utterances.’ “

After MIT closed in March, she was able to finish the project from her parents’ home in East Hanover, New Jersey. “Regular meetings with my advisor have been really helpful in keeping me motivated and on track,” she says. She says she also got to improve her web-development skills, which will come in handy when she starts work at Benchling, a San Francisco-based software company, this summer.

Spring semester Quest UROP projects were funded, in part, by the MIT-IBM Watson AI Lab and Eric Schmidt, technical advisor to Alphabet Inc., and his wife, Wendy.

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Faculty receive funding to develop artificial intelligence techniques to combat Covid-19 | MIT News

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Artificial intelligence has the power to help put an end to the Covid-19 pandemic. Not only can techniques of machine learning and natural language processing be used to track and report Covid-19 infection rates, but other AI techniques can also be used to make smarter decisions about everything from when states should reopen to how vaccines are designed. Now, MIT researchers working on seven groundbreaking projects on Covid-19 will be funded to more rapidly develop and apply novel AI techniques to improve medical response and slow the pandemic spread.

Earlier this year, the C3.ai Digital Transformation Institute (C3.ai DTI) formed, with the goal of attracting the world’s leading scientists to join in a coordinated and innovative effort to advance the digital transformation of businesses, governments, and society. The consortium is dedicated to accelerating advances in research and combining machine learning, artificial intelligence, internet of things, ethics, and public policy — for enhancing societal outcomes. MIT, under the auspices of the School of Engineering, joined the C3.ai DTI consortium, along with C3.ai, Microsoft Corporation, the University of Illinois at Urbana-Champaign, the University of California at Berkeley, Princeton University, the University of Chicago, Carnegie Mellon University, and, most recently, Stanford University.

The initial call for project proposals aimed to embrace the challenge of abating the spread of Covid-19 and advance the knowledge, science, and technologies for mitigating the impact of pandemics using AI. Out of a total of 200 research proposals, 26 projects were selected and awarded $5.4 million to continue AI research to mitigate the impact of Covid-19 in the areas of medicine, urban planning, and public policy.

The first round of grant recipients was recently announced, and among them are five projects led by MIT researchers from across the Institute: Saurabh Amin, associate professor of civil and environmental engineering; Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management; Munther Dahleh, the William A. Coolidge Professor of Electrical Engineering and Computer Science and director of the MIT Institute for Data, Systems, and Society; David Gifford, professor of biological engineering and of electrical engineering and computer science; and Asu Ozdaglar, the MathWorks Professor of Electrical Engineering and Computer Science, head of the Department of Electrical Engineering and Computer Science, and deputy dean of academics for MIT Schwarzman College of Computing.

“We are proud to be a part of this consortium, and to collaborate with peers across higher education, industry, and health care to collectively combat the current pandemic, and to mitigate risk associated with future pandemics,” says Anantha P. Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “We are so honored to have the opportunity to accelerate critical Covid-19 research through resources and expertise provided by the C3.ai DTI.”

Additionally, three MIT researchers will collaborate with principal investigators from other institutions on projects blending health and machine learning. Regina Barzilay, the Delta Electronics Professor in the Department of Electrical Engineering and Computer Science, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science, join Ziv Bar-Joseph from Carnegie Mellon University for a project using machine learning to seek treatment for Covid-19. Aleksander Mądry, professor of computer science in the Department of Electrical Engineering and Computer Science, joins Sendhil Mullainathan of the University of Chicago for a project using machine learning to support emergency triage of pulmonary collapse due to Covid-19 on the basis of X-rays.

Bertsimas’s project develops automated, interpretable, and scalable decision-making systems based on machine learning and artificial intelligence to support clinical practices and public policies as they respond to the Covid-19 pandemic. When it comes to reopening the economy while containing the spread of the pandemic, Ozdaglar’s research provides quantitative analyses of targeted interventions for different groups that will guide policies calibrated to different risk levels and interaction patterns. Amin is investigating the design of actionable information and effective intervention strategies to support safe mobilization of economic activity and reopening of mobility services in urban systems. Dahleh’s research innovatively uses machine learning to determine how to safeguard schools and universities against the outbreak. Gifford was awarded funding for his project that uses machine learning to develop more informed vaccine designs with improved population coverage, and to develop models of Covid-19 disease severity using individual genotypes.

“The enthusiastic support of the distinguished MIT research community is making a huge contribution to the rapid start and significant progress of the C3.ai Digital Transformation Institute,” says Thomas Siebel, chair and CEO of C3.ai. “It is a privilege to be working with such an accomplished team.”

The following projects are the MIT recipients of the inaugural C3.ai DTI Awards: 

“Pandemic Resilient Urban Mobility: Learning Spatiotemporal Models for Testing, Contact Tracing, and Reopening Decisions” — Saurabh Amin, associate professor of civil and environmental engineering; and Patrick Jaillet, the Dugald C. Jackson Professor of Electrical Engineering and Computer Science

“Effective Cocktail Treatments for SARS-CoV-2 Based on Modeling Lung Single Cell Response Data” — Regina Barzilay, the Delta Electronics Professor in the Department of Electrical Engineering and Computer Science, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science (Principal investigator: Ziv Bar-Joseph of Carnegie Mellon University)

“Toward Analytics-Based Clinical and Policy Decision Support to Respond to the Covid-19 Pandemic” — Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management and associate dean for business analytics; and Alexandre Jacquillat, assistant professor of operations research and statistics

“Reinforcement Learning to Safeguard Schools and Universities Against the Covid-19 Outbreak” — Munther Dahleh, the William A. Coolidge Professor of Electrical Engineering and Computer Science and director of MIT Institute for Data, Systems, and Society; and Peko Hosoi, the Neil and Jane Pappalardo Professor of Mechanical Engineering and associate dean of engineering

“Machine Learning-Based Vaccine Design and HLA Based Risk Prediction for Viral Infections” — David Gifford, professor of biological engineering and of electrical engineering and computer science

“Machine Learning Support for Emergency Triage of Pulmonary Collapse in Covid-19” — Aleksander Mądry, professor of computer science in the Department of Electrical Engineering and Computer Science (Principal investigator: Sendhil Mullainathan of the University of Chicago)

“Targeted Interventions in Networked and Multi-Risk SIR Models: How to Unlock the Economy During a Pandemic” — Asu Ozdaglar, the MathWorks Professor of Electrical Engineering and Computer Science, department head of electrical engineering and computer science, and deputy dean of academics for MIT Schwarzman College of Computing; and Daron Acemoglu, Institute Professor

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