Aurelia Butler

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  • 113 Shares149 Views

    in Computers Math

    Can’t find your phone? There’s a robot for that

    Engineers at the University of Waterloo have discovered a new way to program robots to help people with dementia locate medicine, glasses, phones and other objects they need but have lost.
    And while the initial focus is on assisting a specific group of people, the technology could someday be used by anyone who has searched high and low for something they’ve misplaced.
    “The long-term impact of this is really exciting,” said Dr. Ali Ayub, a post-doctoral fellow in electrical and computer engineering. “A user can be involved not just with a companion robot but a personalized companion robot that can give them more independence.”
    Ayub and three colleagues were struck by the rapidly rising number of people coping with dementia, a condition that restricts brain function, causing confusion, memory loss and disability. Many of these individuals repeatedly forget the location of everyday objects, which diminishes their quality of life and places additional burdens on caregivers.
    Engineers believed a companion robot with an episodic memory of its own could be a game-changer in such situations. And they succeeded in using artificial intelligence to create a new kind of artificial memory.
    The research team began with a Fetch mobile manipulator robot, which has a camera for perceiving the world around it.
    Next, using an object-detection algorithm, they programmed the robot to detect, track and keep a memory log of specific objects in its camera view through stored video. With the robot capable of distinguishing one object from another, it can record the time and date objects enter or leave its view.
    Researchers then developed a graphical interface to enable users to choose objects they want to be tracked and, after typing the objects’ names, search for them on a smartphone app or computer. Once that happens, the robot can indicate when and where it last observed the specific object.
    Tests have shown the system is highly accurate. And while some individuals with dementia might find the technology daunting, Ayub said caregivers could readily use it.
    Moving forward, researchers will conduct user studies with people without disabilities, then people with dementia.
    A paper on the project, Where is my phone? Towards developing an episodic memory model for companion robots to track users’ salient objects, was presented at the recent 2023 ACM/IEEE International Conference on Human-Robot Interaction. More

  • 100 Shares179 Views

    in Computers Math

    Tetris reveals how people respond to unfair AI

    A Cornell University-led experiment in which two people play a modified version of Tetris revealed that players who get fewer turns perceived the other player as less likable, regardless of whether a person or an algorithm allocated the turns.
    Most studies on algorithmic fairness focus on the algorithm or the decision itself, but researchers sought to explore the relationships among the people affected by the decisions.
    “We are starting to see a lot of situations in which AI makes decisions on how resources should be distributed among people,” said Malte Jung, associate professor of information science, whose group conducted the study. “We want to understand how that influences the way people perceive one another and behave towards each other. We see more and more evidence that machines mess with the way we interact with each other.”
    In an earlier study, a robot chose which person to give a block to and studied the reactions of each individual to the machine’s allocation decisions.
    “We noticed that every time the robot seemed to prefer one person, the other one got upset,” said Jung. “We wanted to study this further, because we thought that, as machines making decisions becomes more a part of the world — whether it be a robot or an algorithm — how does that make a person feel?”
    Using open-source software, Houston Claure — the study’s first author and postdoctoral researcher at Yale University — developed a two-player version of Tetris, in which players manipulate falling geometric blocks in order to stack them without leaving gaps before the blocks pile to the top of the screen. Claure’s version, Co-Tetris, allows two people (one at a time) to work together to complete each round.
    An “allocator” — either human or AI, which was conveyed to the players — determines which player takes each turn. Jung and Claure devised their experiment so that players would have either 90% of the turns (the “more” condition), 10% (“less”) or 50% (“equal”).
    The researchers found, predictably, that those who received fewer turns were acutely aware that their partner got significantly more. But they were surprised to find that feelings about it were largely the same regardless of whether a human or an AI was doing the allocating.
    The effect of these decisions is what the researchers have termed “machine allocation behavior” — similar to the established phenomenon of “resource allocation behavior,” the observable behavior people exhibit based on allocation decisions. Jung said machine allocation behavior is “the concept that there is this unique behavior that results from a machine making a decision about how something gets allocated.”
    The researchers also found that fairness didn’t automatically lead to better game play and performance. In fact, equal allocation of turns led, on average, to a worse score than unequal allocation.
    “If a strong player receives most of the blocks,” Claure said, “the team is going to do better. And if one person gets 90%, eventually they’ll get better at it than if two average players split the blocks.” More

  • 138 Shares129 Views

    in Computers Math

    Students positive towards AI, but uncertain about what counts as cheating

    Students in Sweden are positive towards AI tools such as ChatGPT in education, but 62 percent believe that using chatbots during exams is cheating. However, where the boundary for cheating lies is highly unclear. This is shown in a survey from Chalmers University of Technology, which is the first large-scale study in Europe to investigate students’ attitudes towards artificial intelligence in higher education.
    “I am afraid of AI and what it could mean for the future.”
    “Don’t worry so much! Keep up with the development and adapt your teaching for the future.”
    “ChatGPT and similar tools will revolutionise how we learn, and we will be able to come up with amazing things.”
    These are three out of nearly two thousand optional comments from the survey which almost 6,000 students in Sweden recently participated in.
    “The students express strong, diverse, and in many cases emotionally charged opinions,” says Hans Malmström, Professor at the Department of Communication and Learning in Science at Chalmers University of technology. He, together with his colleagues Christian Stöhr and Amy Wanyu Ou, conducted the study.

    More than a third use ChatGPT regularly
    A majority of the respondents believe that chatbots and AI language tools make them more efficient as students and argue that such tools improve their academic writing and overall language skills. Virtually all the responding students are familiar with ChatGPT, the majority use the tool, and 35 percent use the chatbot regularly.
    Lack guidance — opposed a ban
    Despite their positive attitude towards AI, many students feel anxious and lack clear guidance on how to use AI in the learning environments they are in. It is simply difficult to know where the boundary for cheating lies.
    “Most students have no idea whether their educational institution has any rules or guidelines for using AI responsibly, and that is of course worrying. At the same time, an overwhelming majority is against a ban on AI in educational contexts,” says Hans Malmström.

    No replacement for critical thinking
    Many students perceive chatbots as a mentor or teacher that they can ask questions or get help from, for example, with explanations of concepts and summaries of ideas. The dominant attitude is that chatbots should be used as an aid, not replace students’ own critical thinking. Or as one student put it: “You should be able to do the same things as the AI, but it should help you do it. You should not use a calculator if you don’t know what the plus sign on it does.”
    Aid in case of disabilities
    Another important aspect that emerged in the survey was that AI serves as an effective aid for people with various disabilities. A student with ADD and dyslexia described how they had spent 20 minutes writing down their answer in the survey and then improved it by inputting the text into ChatGPT: “It’s like being color blind and suddenly being able to see all the beautiful colors.”
    Giving students a voice
    The researchers have now gathered a wealth of important information and compiled the results in an overview report.
    “We hope and believe that the answers from this survey will give students a voice and the results will thus be an important contribution to our collective understanding of AI and learning,” says Christian Stöhr, Associate Professor at the Department of Communication and Learning in Science at Chalmers.
    More about the study
    “Chatbots and other AI for learning: A survey on use and views among university students in Sweden” was conducted in the following way: The researchers at Chalmers conducted the survey between 5 April and 5 May, 2023. Students at all universities in Sweden could participate. The survey was distributed through social media and targeted efforts from multiple universities and student organisations. In total, the survey was answered by 5,894 students.
    Summary of results: 95 percent of students are familiar with ChatGPT, while awareness of other chatbots is very low. 56 percent are positive about using chatbots in their studies; 35 percent use ChatGTP regularly. 60 percent are opposed to a ban on chatbots, and 77 percent are against a ban on other AI tools (such as Grammarly) in education. More than half of the students do not know if their institution has guidelines for how AI can be used in education; one in four explicitly says that their institution lack such regulations. 62 percent believe that using chatbots during examinations is cheating. Students express some concern about AI development, and there is particular concern over the impact of chatbots on future education. More

  • 125 Shares119 Views

    in Heart

    There’s good and bad news with California’s electric vehicle program

    A worldwide gearshift from fossil fuel–powered cars to electric vehicles could significantly reduce the amount of carbon dioxide that humans emit to the atmosphere. But current strategies for vehicle electrification can also shift some pollution to communities already suffering under higher economic, health and environmental burdens, researchers warn.

    California, which leads the United States by a mile when it comes to EV adoption, offers a window into this evolving problem. The state is aggressively seeking to reduce its carbon footprint and has made substantial increases in wind and solar power generation as well as in the promotion of electric vehicle purchases. One tool the state has used is the California Clean Vehicle Rebate Project, or CVRP, which kicked off in 2010 and offers consumers money back for the purchase or lease of new EVs.

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    Now, an analysis of the CVRP’s impact on the state’s air quality from 2010 to 2021 reveals both good and bad news, researchers report May 3 in PLOS Climate.

    To assess the impact of the CVRP on a community and statewide level, the team developed a computer model that incorporates data on where the rebates went, how much additional electricity would be required to power those vehicles, which of the state’s electric generating units would provide that power and how much pollution they might produce.

    The team then overlapped these data with a mapping tool called CalEnviroScreen that identifies which of the state’s more than 8,000 census tracts — county subdivisions used in population assessments — are the most vulnerable to pollution. That vulnerability measure is based not only on exposure to pollutants such as power plant emissions and unsafe water but also on factors such as income, education level, access to health care and linguistic isolation.

    The good news is that the CVRP is responsible for making a dent in the state’s overall CO2 emissions, reducing them by about 280,000 metric tons per year on average, says environmental scientist Jaye Mejía-Duwan of the University of California, Berkeley. In 2020, transportation in California produced about 160 million tons of CO2, about 40 percent of the total 370 million tons of CO2 emitted by the state that year.

    The program has also reduced the state’s overall emissions of other types of air-polluting gases, including sulfur dioxide and several nitrogen oxide gases collectively called NOx.

    The bad news is that the most disadvantaged communities in the state didn’t see the same overall improvement in air quality, Mejía-Duwan and colleagues found. Those communities didn’t have the same decreases in sulfur dioxide and NOx gases — and in fact saw an increase in one type of air pollution, tiny particulates known as PM2.5 (SN: 7/30/20). “These particles are small enough to penetrate deep into the lungs and cross over into the bloodstream,” increasing the risk of cancer, cardiovascular problems and cognitive decline, Mejía-Duwan says.  

    Where the power is

    California uses a computer model called CalEnviroScreen, currently in its fourth version, to determine levels of vulnerability to the impacts of pollution. The most disadvantaged communities (darker blue) are determined by both pollutant exposure and socioeconomic factors. The state’s electricity-generating units (EGUs, circles) are disproportionately located in the most disadvantaged communities. That reveals how increasing electrification, which includes power generated by nonrenewable sources, could increase pollution in many of the most disadvantaged communities.

    Pollution impact by community

    J. Mejía-Duwan, M. Hino and K. J. Mach/PLOS Climate 2023

    J. Mejía-Duwan, M. Hino and K. J. Mach/PLOS Climate 2023

    That increase may be indirectly related to putting more EVs on the road. Although electric vehicles themselves don’t produce PM2.5 from their tailpipes, increased electricity generation, if it’s not fossil fuel–free, can. Renewable resources, including rooftop solar cells, supplied about half of California’s electricity in 2022. But natural gas–fired power plants still provide a hefty chunk of the state’s power.

    “Electric vehicles are often incorrectly referred to as ‘zero-emission vehicles,’ but they’re only as clean as the underlying electric grid from which the energy is sourced,” Mejía-Duwan says. The most disadvantaged 25 percent of the state’s communities also contain 50 percent of the power plants, the team found.

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    EVs also tend to be relatively heavy due to their hefty batteries. And “heavier vehicles can produce as much if not more particulate matter” than equivalently sized fossil fuel-powered cars, due to brake, tire or road wear, Mejía-Duwan says.

    Increasing the cleanliness of the electric grid would help, as would changes to the management of the state’s generated power, the researchers say. California’s solar, wind and hydroelectric energy production has grown rapidly. But the battery technology to store and use that energy later lags behind. Most of that energy is generated during the day, so some researchers have suggested plugging in electric vehicles while it’s light out to take advantage of the daytime glut of electricity — and then using the vehicles to help power houses at nighttime (SN: 12/22/21).

    But, clever as that idea is, it doesn’t address the underlying factors behind these inequities. Since 2010, the CVRP has provided over 400,000 rebates for EVs of up to $7,500, depending on income. Yet, as it turns out, those rebates have disproportionately gone to the least disadvantaged communities. “That’s a major driver of these inequities,” Mejía-Duwan says.

    Changing that isn’t an easy fix. The state has tried several ways to address the issue, such as by imposing an income cap on eligibility.

    But those efforts have had little effect, particularly given strong barriers that stand in the way of the adoption of EVs by people in disadvantaged communities. One roadblock is that prospective EV buyers must have enough money for a down payment, and then fill out forms and be able to wait several months for the rebate money. Another is that car manufacturers are trending toward producing larger, more expensive EVs. Chevrolet, for example, announced in April that its most affordable EV, the Bolt, will be discontinued as the company pivots to producing electric SUVs.

    There’s also a lack of equitable access to vehicle charging infrastructure. And then there are subtler but no less insidious issues, such as “a lack of sufficient multicultural and multilingual outreach about EVs, plus the fact that people of color and minoritized communities report facing discrimination at dealerships,” Mejía-Duwan says.

    These findings echo and support researchers’ longtime concerns about how current programs to encourage vehicle electrification will disproportionately impact people. “It’s not a surprise,” says Román Partida-López, senior legal counsel for transportation equity at The Greenlining Institute, a nonprofit organization based in Oakland, Calif. “What [California] is doing is a move in the right direction, but it’s not enough.”

    California and other states pursuing aggressive zero-emissions policies need to shift their thinking, Partida-López says, to be more intentional about targeting their efforts toward the communities experiencing the greatest impacts (SN: 12/14/22). Rebates, in particular, are known to be an inequitable approach, he says, because they “assume you have the money up front to be able to put down a down payment of several thousand dollars.”

    A better strategy to reduce the barriers to EV adoption, he says, would be to provide other types of incentives, such as vouchers that low-income households could use at the time of purchase as well as accessible financing programs.

    After all, making EVs accessible to everyone is going to be essential to the big picture of transitioning to zero emissions (SN: 1/27/23). “We’re not going to meet any of those goals unless we center equity” in program designs, Partida-López says. “The focus has always been, ‘How do we transform the market?’ We need to change the narrative to ‘How are we going to focus on the people most impacted, to help with this transition?’”

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    In one lake deep under Antarctica’s ice, microbes feast on ancient carbon

    How microbes survive in lakes far beneath Antarctica’s ice sheet has been a mystery. Now scientists have figured out what’s on the menu for microbes in one buried lake in West Antarctica.

    The lake’s bacteria and other microbial inhabitants get by on carbon that seawater left behind thousands of years ago, researchers report in the April AGU Advances. The find adds to existing evidence that, during a period of warming about 6,000 years ago, the ice sheet in West Antarctica was smaller than it is today. That allowed seawater to deposit nutrients in what is now a lake bed buried under hundreds of meters of ice.

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    This study is among the first to provide evidence from beneath the ice that the ice sheet was smaller in the not-so-distant past, geologically speaking, before growing back to its modern size, says Greg Balco, a geochemist at the Berkeley Geochronology Center in California.

    Understanding how the ice sheet changed during past periods of warming is crucial to predicting Antarctica’s future as the world continues to warm due to human-caused climate change, says Balco, who was not involved in the new study.

    Hundreds of lakes pool under Antarctica’s massive ice sheet, the result of the underside of the ice ever-so-slowly melting due to heat from the Earth’s interior. The lakes tend to be pitch-black, near freezing and are almost all isolated from the outside world.

    These less-than-ideal conditions should make them hostile to life. “If I were a microbe, I wouldn’t want to live in the cold, dark depths where I haven’t seen the sun or a new nutrient in thousands of years,” says Ryan Venturelli, a paleoglaciologist at the Colorado School of Mines in Golden. Yet billions of microbes — and even some animals — have found a way to thrive in these subglacial waterbodies (SN: 4/21/23).

    As the last glacial period came to a close, starting around 15,000 years ago, ice sheets around the world retreated. Computer simulations have predicted that as the climate warmed, the ice sheet in West Antarctica may have shrunk to an even smaller size than it is today. But working out what a smaller ice sheet might have looked like isn’t easy since most of the evidence for it is now locked under ice, Balco says.

    In 2018, Venturelli joined a team of about 30 scientists headed to a remote corner of West Antarctica to drill for the past. The journey took them to Lake Mercer: a body of subglacial water that today sits 150 kilometers from the sea.

    It took the expedition over a week of using a hot-water drill 24 hours a day to pierce through just over a kilometer of ice to reach the lake. “There was a lot of cheering and high-fiving” when the drill finally made it through, Venturelli recalls. Lake Mercer is only the second subglacial lake in the world that scientists have ever managed to reach.

    Paleoglaciologist Ryan Venturelli holds a tube of sediment collected from the bed of Lake Mercer. Carbon in this sediment core reveals that the lake was connected to the ocean 6,000 years ago.Billy Collins

    The team collected and analyzed lake water and sediment samples from the lake bed. This work revealed traces of 6,000-year-old carbon-14, a form of the element that’s made in the atmosphere and then falls to Earth. For that carbon to get past the ice, the lake would have had to have contact with the outside world. 

    The researchers didn’t spot any telltale signs of ancient photosynthesizing plankton, suggesting that the area wasn’t open ocean when the carbon settled in the sediment. Instead, seawater carrying the carbon must have come to the lake. That means that ocean water had to have flowed under the ice about 250 kilometers farther inland than it does today, the researchers say. 

    “There’s no other way to get carbon-14 in there,” Balco says. “You can’t push it through ice. Organisms can’t tunnel through. The only way for it to get it there is for ocean water to get under the ice sheet.”

    Seawater does flow under the ice today — but not as far inland as the lake’s location. So the edge of the ice shelf was probably closer to Lake Mercer several thousand years ago. That suggests, the team says, that the ice sheet over West Antarctica was probably smaller back then.

    Microorganisms living in this area 6,000 years ago would have feasted on the inflow from the ocean. And their descendants still seem to as well. The researchers found traces of carbon-14 in the water samples as well as in the sediment, suggesting the microbes are recycling the ancient carbon deposited in the lake bed as food.

    The new study emphasizes how much information is waiting to be found in Antarctica’s hidden lakes, Venturelli says. “There are about 675 lakes under the ice sheet, and we’ve only sampled two,” she says. “I would very much like to drill into every single one of them.” More

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    in Computers Math

    Better than humans: Artificial intelligence in intensive care units

    In the future, artificial intelligence will play an important role in medicine. In diagnostics, successful tests have already been performed: for example, the computer can learn to categorise images with great accuracy according to whether they show pathological changes or not. However, it is more difficult to train an artificial intelligence to examine the time-varying conditions of patients and to calculate treatment suggestions — this is precisely what has now been achieved at TU Wien in cooperation with the Medical University of Vienna.
    With the help of extensive data from intensive care units of various hospitals, an artificial intelligence was developed that provides suggestions for the treatment of people who require intensive care due to sepsis. Analyses show that artificial intelligence already surpasses the quality of human decisions. However, it is now important to also discuss the legal aspects of such methods.
    Making optimal use of existing data
    “In an intensive care unit, a lot of different data is collected around the clock. The patients are constantly monitored medically. We wanted to investigate whether these data could be used even better than before,” says Prof. Clemens Heitzinger from the Institute for Analysis and Scientific Computing at TU Wien (Vienna). He is also Co-Director of the cross-faculty “Center for Artificial Intelligence and Machine Learning” (CAIML) at TU Wien.
    Medical staff make their decisions on the basis of well-founded rules. Most of the time, they know very well which parameters they have to take into account in order to provide the best care. However, the computer can easily take many more parameters than a human into account — and in some cases this can lead to even better decisions.
    The computer as planning agent
    “In our project, we used a form of machine learning called reinforcement learning,” says Clemens Heitzinger. “This is not just about simple categorisation — for example, separating a large number of images into those that show a tumour and those that do not — but about a temporally changing progression, about the development that a certain patient is likely to go through. Mathematically, this is something quite different. There has been little research in this regard in the medical field.”

    The computer becomes an agent that makes its own decisions: if the patient is well, the computer is “rewarded.” If the condition deteriorates or death occurs, the computer is “punished.” The computer programme has the task of maximising its virtual “reward” by taking actions. In this way, extensive medical data can be used to automatically determine a strategy which achieves a particularly high probability of success.
    Already better than a human
    “Sepsis is one of the most common causes of death in intensive care medicine and poses an enormous challenge for doctors and hospitals, as early detection and treatment is crucial for patient survival,” says Prof. Oliver Kimberger from the Medical University of Vienna. “So far, there have been few medical breakthroughs in this field, which makes the search for new treatments and approaches all the more urgent. For this reason, it is particularly interesting to investigate the extent to which artificial intelligence can contribute to improve medical care here. Using machine learning models and other AI technologies are an opportunity to improve the diagnosis and treatment of sepsis, ultimately increasing the chances of patient survival.”
    Analysis shows that AI capabilities are already outperforming humans: “Cure rates are now higher with an AI strategy than with purely human decisions. In one of our studies, the cure rate in terms of 90-day mortality was increased by about 3% to about 88%,” says Clemens Heitzinger.
    Of course, this does not mean that one should leave medical decisions in an intensive care unit to the computer alone. But the artificial intelligence may run along as an additional device at the bedside — and the medical staff can consult it and compare their own assessment with the artificial intelligence’s suggestions. Such artificial intelligences can also be highly useful in education.
    Discussion about legal issues is necessary
    “However, this raises important questions, especially legal ones,” says Clemens Heitzinger. “One probably thinks of the question who will be held liable for any mistakes made by the artificial intelligence first. But there is also the converse problem: what if the artificial intelligence had made the right decision, but the human chose a different treatment option and the patient suffered harm as a result?” Does the doctor then face the accusation that it would have been better to trust the artificial intelligence because it comes with a huge wealth of experience? Or should it be the human’s right to ignore the computer’s advice at all times?
    “The research project shows: artificial intelligence can already be used successfully in clinical practice with today’s technology — but a discussion about the social framework and clear legal rules are still urgently needed,” Clemens Heitzinger is convinced. More

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    in Computers Math

    Robotic proxy brings remote users to life in real time

    Cornell University researchers have developed a robot, called ReMotion, that occupies physical space on a remote user’s behalf, automatically mirroring the user’s movements in real time and conveying key body language that is lost in standard virtual environments.
    “Pointing gestures, the perception of another’s gaze, intuitively knowing where someone’s attention is — in remote settings, we lose these nonverbal, implicit cues that are very important for carrying out design activities,” said Mose Sakashita, a doctoral student of information science.
    Sakashita is the lead author of “ReMotion: Supporting Remote Collaboration in Open Space with Automatic Robotic Embodiment,” which he presented at the Association for Computing Machinery CHI Conference on Human Factors in Computing Systems in Hamburg, Germany. “With ReMotion, we show that we can enable rapid, dynamic interactions through the help of a mobile, automated robot.”
    The lean, nearly six-foot-tall device is outfitted with a monitor for a head, omnidirectional wheels for feet and game-engine software for brains. It automatically mirrors the remote user’s movements — thanks to another Cornell-made device, NeckFace, which the remote user wears to track head and body movements. The motion data is then sent remotely to the ReMotion robot in real-time.
    Telepresence robots are not new, but remote users generally need to steer them manually, distracting from the task at hand, researchers said. Other options such as virtual reality and mixed reality collaboration can also require an active role from the user and headsets may limit peripheral awareness, researchers added.
    In a small study, nearly all participants reported having a better connection with their remote teammates when using ReMotion compared to an existing telerobotic system. Participants also reported significantly higher shared attention among remote collaborators.
    In its current form, ReMotion only works with two users in a one-on-one remote environment, and each user must occupy physical spaces of identical size and layout. In future work, ReMotion developers intend to explore asymmetrical scenarios, like a single remote team member collaborating virtually via ReMotion with multiple teammates in a larger room.
    With further development, Sakashita says ReMotion could be deployed in virtual collaborative environments as well as in classrooms and other educational settings.
    This research was funded in part by the National Science Foundation and the Nakajima Foundation. More

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    in Computers Math

    Researcher uses artificial intelligence to discover new materials for advanced computing

    A team of researchers led by Rensselaer Polytechnic Institute’s Trevor David Rhone, assistant professor in the Department of Physics, Applied Physics, and Astronomy, has identified novel van der Waals (vdW) magnets using cutting-edge tools in artificial intelligence (AI). In particular, the team identified transition metal halide vdW materials with large magnetic moments that are predicted to be chemically stable using semi-supervised learning. These two-dimensional (2D) vdW magnets have potential applications in data storage, spintronics, and even quantum computing.
    Rhone specializes in harnessing materials informatics to discover new materials with unexpected properties that advance science and technology. Materials informatics is an emerging field of study at the intersection of AI and materials science. His team’s latest research was recently featured on the cover of Advanced Theory and Simulations.
    2D materials, which can be as thin as a single atom, were only discovered in 2004 and have been the subject of great scientific curiosity because of their unexpected properties. 2D magnets are significant because their long-range magnetic ordering persists when they are thinned down to one or a few layers. This is due to magnetic anisotropy. The interplay with this magnetic anisotropy and low dimensionality could give rise to exotic spin degrees of freedom, such as spin textures that can be used in the development of quantum computing architectures. 2D magnets also span the full range of electronic properties and can be used in high-performance and energy-efficient devices.
    Rhone and team combined high-throughput density functional theory (DFT) calculations, to determine the vdW materials’ properties, with AI to implement a form of machine learning called semi-supervised learning. Semi-supervised learning uses a combination of labeled and unlabeled data to identify patterns in data and make predictions. Semi-supervised learning mitigates a major challenge in machine learning — the scarcity of labeled data.
    “Using AI saves time and money,” said Rhone. “The typical materials discovery process requires expensive simulations on a supercomputer that can take months. Lab experiments can take even longer and can be more expensive. An AI approach has the potential to speed up the materials discovery process.”
    Using an initial subset of 700 DFT calculations on a supercomputer, an AI model was trained that could predict the properties of many thousands of materials candidates in milliseconds on a laptop. The team then identified promising candidate vdW materials with large magnetic moments and low formation energy. Low formation energy is an indicator of chemical stability, which is an important requirement for synthesizing the material in a laboratory and subsequent industrial applications.
    “Our framework can easily be applied to explore materials with different crystal structures, as well,” said Rhone. “Mixed crystal structure prototypes, such as a data set of both transition metal halides and transition metal trichalcogenides, can also be explored with this framework.”
    “Dr. Rhone’s application of AI to the field of materials science continues to produce exciting results,” said Curt Breneman, dean of Rensselaer’s School of Science. “He has not only accelerated our understanding of 2D materials that have novel properties, but his findings and methods are likely to contribute to new quantum computing technologies.”
    Rhone was joined in research by Romakanta Bhattarai and Haralambos Gavras of Renselaer; Bethany Lusch and Misha Salim of Argonne National Laboratory; Marios Mattheakis, Daniel T. Larson, and Efthimios Kaxiras of Harvard University; and Yoshiharu Krockenberger of NTT Basic Research Laboratories. More