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      Making each vote count

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      Graduate student Jacob Jaffe wants to improve the administration of American elections. To do that, he is posing “questions in political science that we haven’t been asking enough,” he says, “and solving them with methods we haven’t been using enough.”

      Considerable research has been devoted to understanding “who votes, and what makes people vote or not vote,” says Jaffe. He is training his attention on questions of a different nature: Does providing practical information to voters about how to cast their ballots change how they will vote? Is it possible to increase the accuracy of vote-counting, on a state-by-state and even precinct-by-precinct basis? How do voters experience polling places? These problems form the core of his dissertation.

      Taking advantage of the resources at the MIT Election Data and Science Lab, where he serves as a researcher, Jaffe conducts novel field experiments to gather highly detailed information on local, state, and federal elections, and analyzes this trove with advanced statistical techniques. Whether investigating the probability of miscounts in voting, or the possibility of changing a voter’s mode of voting, Jaffe intends to strengthen the scaffolding that supports representative government. “Elections are both theoretically and normatively important; they’re the basis of our belief in the moral rightness of the state to do the things the state does,” he says.

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      For one of his keystone projects, Jaffe seized a unique opportunity to run a big field experiment. In summer 2020, at the height of the Covid-19 pandemic, he emailed 80,000 Floridians instructions on how to vote in an upcoming primary by mail. His email contained a link enabling recipients to fill out two simple questions to receive a ballot. “I wanted to learn if this was an effective method for getting people to vote by mail, and I proved it is, statistically,” he says. “This is important to know because if elections are held in times when we might need people to vote nonlocally or vote using one method over another — if they’re displaced by a hurricane or another emergency, for instance — I learned that we can effect a new vote mode practically and quickly.”

      One of Jaffe’s insights from this experiment is that “people do read their voting-related emails, but the content of the email has to be something they can act on proximately,” he says. “A message reminding them to vote two weeks from now is not so helpful.” The lower the burden on an individual to participate in voting, whether due to proximity to a polling site or instructions on how to receive and cast a ballot, the greater the likelihood of that person engaging in the election.

      “If we want people to vote by mail, we need to reduce the informational cost so it’s easier for voters to understand how the system works,” he says.

      Another significant research thrust for Jaffe involves scrutinizing accuracy in vote counting, using instances of recounts in presidential elections. Ensuring each vote counts, he says, “is one of the most fundamental questions in democracy,” he says.

      With access to 20 elections in 2020, Jaffe is comparing original vote totals for each candidate to the recounted, correct tally, on a precinct-level basis. “Using original combinatorial techniques, I can estimate the probability of miscounting ballots,” he says. The ultimate goal is to generate a granular picture of the efficacy of election administration across the country.

      “It varies a lot by state, and most states do a good job,” he says. States that take their time in counting perform better. “There’s a phenomenon where some towns race to get results in as quickly as possible, and this affects their accuracy.”

      In spite of the bright spots, Jaffe sees chronic underfunding of American elections. “We need to give local administrators the resources, the time and money to fund employees to do their jobs,” he says. The worse the situation is, “the more likely that elections will be called wrong, with no one knowing.” Jaffe believes that his analysis can offer states useful information for improving election administration. “Determining how good a place is historically at counting ballots can help determine the likelihood of needing costly recounts in future elections,” he says.

      The ballot box and beyond

      It didn’t take Jaffe long to decide on a life dedicated to studying politics. Part of a Boston-area family who, he says, “liked discussing what was going on in the world,” he had his own subscriptions to Time magazine at age 9, and to The Economist in middle school. During high school, he volunteered for then-Massachusetts Representative Barney Frank and Senator John Kerry, working on constituent services. At Rice University, he interned all four years with political scientist Robert M. Stein, an expert on voting and elections. With Stein’s help, Jaffe landed a position the summer before his senior year with the Department of Justice (DOJ), researching voting rights cases.

      “The experience was fascinating, and the work felt super important,” says Jaffe. His portfolio involved determining whether legal challenges to particular elections met the statistical standard for racial gerrymandering. “I had to answer hard quantitative questions about the relationship between race and voting in an area, and whether minority candidates were systematically prevented from winning,” he says.

      But while Jaffe cared a lot about this work, he didn’t feel adequately challenged. “As a 21-year-old at DOJ, I learned that I could address problems in the world using statistics,” he says. “But I felt I could have a greater impact addressing tougher questions outside of voting rights.”

      Jaffe was drawn to political science at MIT, and specifically to the research of Charles Stewart III, the Kenan Sahin Distinguished Professor of Political Science, director of the MIT Election Lab, and head of Jaffe’s thesis committee. It wasn’t just the opportunity to plumb the lab’s singular repository of voting data that attracted Jaffe, but its commitment to making every vote count. For Jaffe, this was a call to arms to investigate the many, and sometimes quotidian, obstacles, between citizens and ballot boxes.

      To this end, he has been analyzing, with the help of mathematical methods from queuing theory, why some elections involve wait lines of six hours and longer at polling sites. “We know that simpler ballots mean people move don’t get stuck in these lines, where they might potentially give up before voting,” he says. “Looking at the content of ballots and the interval between voter check-in and check-out, I learned that adding races, rather than candidates, to a ballot, means that people take more time completing ballots, leading to interminable lines.”

      A key takeaway from his ensemble of studies is that “while it’s relatively rare that elections are bad, we shouldn’t think that we’re good to go,” he says. “Instead, we need to be asking under what conditions do things get bad, and how can we make them better.” More

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      Costis Daskalakis appointed inaugural Avanessians Professor in the MIT Schwarzman College of Computing

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      The MIT Stephen A. Schwarzman College of Computing has named Costis Daskalakis as the inaugural holder of the Avanessians Professorship. His chair began on July 1.

      Daskalakis is the first person appointed to this position generously endowed by Armen Avanessians ’81. Established in the MIT Schwarzman College of Computing, the new chair provides Daskalakis with additional support to pursue his research and develop his career.

      “I’m delighted to recognize Costis for his scholarship and extraordinary achievements with this distinguished professorship,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science.

      A professor in the MIT Department of Electrical Engineering and Computer Science, Daskalakis is a theoretical computer scientist who works at the interface of game theory, economics, probability theory, statistics, and machine learning. He has resolved long-standing open problems about the computational complexity of the Nash equilibrium, the mathematical structure and computational complexity of multi-item auctions, and the behavior of machine-learning methods such as the expectation-maximization algorithm. He has obtained computationally and statistically efficient methods for statistical hypothesis testing and learning in high-dimensional settings, as well as results characterizing the structure and concentration properties of high-dimensional distributions. His current work focuses on multi-agent learning, learning from biased and dependent data, causal inference, and econometrics.

      A native of Greece, Daskalakis joined the MIT faculty in 2009. He is a member of the Computer Science and Artificial Intelligence Laboratory and is affiliated with the Laboratory for Information and Decision Systems and the Operations Research Center. He is also an investigator in the Foundations of Data Science Institute.

      He has previously received such honors as the 2018 Nevanlinna Prize from the International Mathematical Union, the 2018 ACM Grace Murray Hopper Award, the Kalai Game Theory and Computer Science Prize from the Game Theory Society, and the 2008 ACM Doctoral Dissertation Award. More

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      Emery Brown wins a share of 2022 Gruber Neuroscience Prize

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      The Gruber Foundation announced on May 17 that Emery N. Brown, the Edward Hood Taplin Professor of Medical Engineering and Computational Neuroscience at MIT, has won the 2022 Gruber Neuroscience Prize along with neurophysicists Laurence Abbott of Columbia University, Terrence Sejnowski of the Salk Institute for Biological Studies, and Haim Sompolinsky of the Hebrew University of Jerusalem.

      The foundation says it honored the four recipients for their influential contributions to the fields of computational and theoretical neuroscience. As datasets have grown ever larger and more complex, these fields have increasingly helped scientists unravel the mysteries of how the brain functions in both health and disease. The prize, which includes a total $500,000 award, will be presented in San Diego, California, on Nov. 13 at the annual meeting of the Society for Neuroscience.

      “These four remarkable scientists have applied their expertise in mathematical and statistical analysis, physics, and machine learning to create theories, mathematical models, and tools that have greatly advanced how we study and understand the brain,” says Joshua Sanes, professor of molecular and cellular biology and founding director of the Center for Brain Science at Harvard University and member of the selection advisory board to the prize. “Their insights and research have not only transformed how experimental neuroscientists do their research, but also are leading to promising new ways of providing clinical care.”

      Brown, who is an investigator in The Picower Institute for Learning and Memory and the Institute for Medical Engineering and Science at MIT, an anesthesiologist at Massachusetts General Hospital, and a professor at Harvard Medical School, says: “It is a pleasant surprise and tremendous honor to be named a co-recipient of the 2022 Gruber Prize in Neuroscience. I am especially honored to share this award with three luminaries in computational and theoretical neuroscience.”

      Brown’s early groundbreaking findings in neuroscience included a novel algorithm that decodes the position of an animal by observing the activity of a small group of place cells in the animal’s brain, a discovery he made while working with fellow Picower Institute investigator Matt Wilson in the 1990s. The resulting state-space algorithm for point processes not only offered much better decoding with fewer neurons than previous approaches, but it also established a new framework for specifying dynamically the relationship between the spike trains (the timing sequence of firing neurons) in the brain and factors from the outside world.

      “One of the basic questions at the time was whether an animal holds a representation of where it is in its mind — in the hippocampus,” Brown says. “We were able to show that it did, and we could show that with only 30 neurons.”

      After introducing this state-space paradigm to neuroscience, Brown went on to refine the original idea and apply it to other dynamic situations — to simultaneously track neural activity and learning, for example, and to define with precision anesthesia-induced loss of consciousness, as well as its subsequent recovery. In the early 2000s, Brown put together a team to specifically study anesthesia’s effects on the brain.

      Through experimental research and mathematical modeling, Brown and his team showed that the altered arousal states produced by the main classes of anesthesia medications can be characterized by analyzing the oscillatory patterns observed in the EEG along with the locations of their molecular targets, and the anatomy and physiology of the neural circuits that connect those locations. He has established, including in recent papers with Picower Professor Earl K. Miller, that a principal way in which anesthetics produce unconsciousness is by producing oscillations that impair how different brain regions communicate with each other.

      The result of Brown’s research has been a new paradigm for brain monitoring during general anesthesia for surgery, one that allows an anesthesiologist to dose the patient based on EEG readouts (neural oscillations) of the patient’s anesthetic state rather than purely on vital sign responses. This pioneering approach promises to revolutionize how anesthesia medications are delivered to patients, and also shed light on other altered states of arousal such as sleep and coma.

      To advance that vision, Brown recently discussed how he is working to develop a new research center at MIT and MGH to further integrate anesthesiology with neuroscience research. The Brain Arousal State Control Innovation Center, he said, would not only advance anesthesiology care but also harness insights gained from anesthesiology research to improve other aspects of clinical neuroscience.

      “By demonstrating that physics and mathematics can make an enormous contribution to neuroscience, doctors Abbott, Brown, Sejnowski, and Sompolinsky have inspired an entire new generation of physicists and other quantitative scientists to follow in their footsteps,” says Frances Jensen, professor and chair of the Department of Neurology and co-director of the Penn Medicine Translational Neuroscience Center within the Perelman School of Medicine at the University of Pennsylvania, and chair of the Selection Advisory Board to the prize. “The ramifications for neuroscience have been broad and profound. It is a great pleasure to be honoring each of them with this prestigious award.”

      This report was adapted from materials provided by the Gruber Foundation. More

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      What words can convey

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      From search engines to voice assistants, computers are getting better at understanding what we mean. That’s thanks to language-processing programs that make sense of a staggering number of words, without ever being told explicitly what those words mean. Such programs infer meaning instead through statistics — and a new study reveals that this computational approach can assign many kinds of information to a single word, just like the human brain.

      The study, published April 14 in the journal Nature Human Behavior, was co-led by Gabriel Grand, a graduate student in electrical engineering and computer science who is affiliated with MIT’s Computer Science and Artificial Intelligence Laboratory, and Idan Blank PhD ’16, an assistant professor at the University of California at Los Angeles. The work was supervised by McGovern Institute for Brain Research investigator Ev Fedorenko, a cognitive neuroscientist who studies how the human brain uses and understands language, and Francisco Pereira at the National Institute of Mental Health. Fedorenko says the rich knowledge her team was able to find within computational language models demonstrates just how much can be learned about the world through language alone.

      The research team began its analysis of statistics-based language processing models in 2015, when the approach was new. Such models derive meaning by analyzing how often pairs of words co-occur in texts and using those relationships to assess the similarities of words’ meanings. For example, such a program might conclude that “bread” and “apple” are more similar to one another than they are to “notebook,” because “bread” and “apple” are often found in proximity to words like “eat” or “snack,” whereas “notebook” is not.

      The models were clearly good at measuring words’ overall similarity to one another. But most words carry many kinds of information, and their similarities depend on which qualities are being evaluated. “Humans can come up with all these different mental scales to help organize their understanding of words,” explains Grand, a former undergraduate researcher in the Fedorenko lab. For example, he says, “dolphins and alligators might be similar in size, but one is much more dangerous than the other.”

      Grand and Blank, who was then a graduate student at the McGovern Institute, wanted to know whether the models captured that same nuance. And if they did, how was the information organized?

      To learn how the information in such a model stacked up to humans’ understanding of words, the team first asked human volunteers to score words along many different scales: Were the concepts those words conveyed big or small, safe or dangerous, wet or dry? Then, having mapped where people position different words along these scales, they looked to see whether language processing models did the same.

      Grand explains that distributional semantic models use co-occurrence statistics to organize words into a huge, multidimensional matrix. The more similar words are to one another, the closer they are within that space. The dimensions of the space are vast, and there is no inherent meaning built into its structure. “In these word embeddings, there are hundreds of dimensions, and we have no idea what any dimension means,” he says. “We’re really trying to peer into this black box and say, ‘is there structure in here?’”

      Specifically, they asked whether the semantic scales they had asked their volunteers use were represented in the model. So they looked to see where words in the space lined up along vectors defined by the extremes of those scales. Where did dolphins and tigers fall on line from “big” to “small,” for example? And were they closer together along that line than they were on a line representing danger (“safe” to “dangerous”)?

      Across more than 50 sets of world categories and semantic scales, they found that the model had organized words very much like the human volunteers. Dolphins and tigers were judged to be similar in terms of size, but far apart on scales measuring danger or wetness. The model had organized the words in a way that represented many kinds of meaning — and it had done so based entirely on the words’ co-occurrences.

      That, Fedorenko says, tells us something about the power of language. “The fact that we can recover so much of this rich semantic information from just these simple word co-occurrence statistics suggests that this is one very powerful source of learning about things that you may not even have direct perceptual experience with.” More

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      Emery Brown earns American Institute for Medical and Biological Engineering Pierre Galletti Award

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      The American Institute for Medical and Biological Engineering has awarded its highest honor this year to Emery N. Brown, the Edward Hood Taplin Professor of Computational Neuroscience and Health Sciences and Technology in The Picower Institute for Learning and Memory and the Institute for Medical Engineering and Science at MIT.

      Brown, who is also an anesthesiologist at Massachusetts General Hospital and the Warren M. Zapol Professor at Harvard Medical School, received the 2022 Pierre M. Galletti Award during the national organization’s Annual Event held on March 25.

      For decades, Brown’s lab has uniquely unified three fields: neuroscience, statistics, and anesthesiology. He is renowned for the development of statistical methods and signal-processing algorithms to enable and improve analysis of neural activity measurements. The work has had numerous applications including studies of learning and memory, brain-computer interfaces, and systems neuroscience. He has also pioneered investigations of how general anesthetic drugs work in the brain to induce and maintain simultaneous but reversible states of unconsciousness, amnesia, immobility, and analgesia. Building on these improvements in fundamental understanding, his lab engineers systems to improve monitoring of patient state and anesthetic dosing during surgery. Optimizing doses of general anesthetic drugs can improve patient care in many ways, including by minimizing side effects such as post-operative delirium and by improving post-operative pain management.

      AIMBE said Brown earned the award in recognition of his “significant contributions to neuroscience data analysis and for characterizing the neurophysiology of anesthesia-induced unconsciousness and demonstrating how it can be reliably monitored in real time using electroencephalogram recordings.”

      Brown, who is also a faculty member in MIT’s Department of Brain and Cognitive Sciences, is now working to develop a research center at MIT dedicated to taking neuroscience-based approaches to advance anesthesiology.

      “I am extremely honored and grateful to the AIMBE for choosing me to receive the 2022 Galletti Award in recognition of my research deciphering the neuroscience of how anesthetics work,” he says. “I would like to express my gratitude to my collaborators, post-doctoral fellows, students, research assistants, and clinical coordinators who have made this possible.” More

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      Professor Emery Brown has big plans for anesthesiology

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      Emery N. Brown — the Edward Hood Taplin Professor of Medical Engineering and of Computational Neuroscience at MIT, an MIT professor of health sciences and technology, an investigator with The Picower Institute for Learning and Memory at MIT, and the Warren M. Zapol Professor of Anaesthesia at Harvard Medical School and Massachusetts General Hospital (MGH) — clearly excels at many roles. Renowned internationally for his anesthesia and neuroscience research, he embodies a unique blend of anesthesiologist, statistician, neuroscientist, educator, and mentor to both students and colleagues. Notably, Brown is one of the most decorated clinician-scientists in the country; he is one of only 25 people — and the first African-American, statistician, and anesthesiologist — to be elected to all three National Academies (Science, Engineering, and Medicine).

      Now, he is handing off one of his many key roles and responsibilities. After almost 10 years, Brown is stepping down as co-director of the Harvard-MIT Program in Health Sciences and Technology (HST). He will turn his energies toward working to develop a new joint center between MIT and MGH that uses the study of anesthesia to design novel approaches to controlling brain states. While a goal of the new center will be to improve anesthesia and intensive care unit management, according to Brown, it will also study related problems such as treating depression, insomnia, and epilepsy, as well as enhancing coma recovery.

      Founded in 1970, HST is one of the oldest interdisciplinary educational programs focused on training the next generation of clinician-scientists and engineers, who learn to translate science, engineering, and medical research into clinical practice, with the aim of improving human health. The MIT Institute for Medical Engineering and Science (IMES), where Brown is associate director, is HST’s home at MIT. Brown was the first HST co-director after the establishment of IMES in 2012; Wolfram Goessling is the Harvard University co-director of HST.

      “Emery has been an exemplary leader for HST during his tenure, and has helped it become a hub for the training of world-class scientists, engineers, and clinicians,” says Anantha Chandrakasan, dean of the MIT School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “I am deeply grateful for his many years of service and wish him well as he moves on to new endeavors.”

      Elazer R. Edelman, director of IMES, calls Brown “a phenom who has been dedicated to our programs for years.”

      “With his thoughtful leadership and understated style, Emery made many contributions to the HST community,” Edelman continues. “On a personal note, this is bittersweet for me, as Emery has been a partner and mentor in my role as IMES director. And while I know that he will always be there for me, as he has been for all of us at IMES and HST, I will miss our late-night calls and midday conferences on matters of import for MIT, IMES, and HST.”

      Brown says “it was an honor and a privilege to co-direct HST with Wolfram.”

      “The students, staff, and faculty are simply amazing,” Brown continues. “Although, now more than 50 years old, HST remains at the vanguard for training PhD and MD students to work at the intersection between engineering, science, and medicine.”

      Goessling also thanks Brown for his leadership: “I truly valued Emery’s partnership and friendship, working together to deepen ties between the MIT and Harvard sides of HST. I am particularly grateful for working with Emery on our combined diversity efforts, leading to the HST Diversity Ambassadors initiative that made HST a better and stronger program.”

      According to Edelman, Brown was instrumental in the transition to new paradigms and relationships with HMS in the context of IMES. In 2014, he led the establishment of clear criteria for HST faculty membership, thereby strengthening the community of faculty experts who train students and provide research opportunities. More recently, he provided guidance through the turmoil of the ongoing Covid-19 pandemic, including the transition to online instruction and the return to the classroom. And Brown has always been a strong supporter of student diversity efforts, serving as an advocate and advisor to HST students.

      Brown holds BA, MA, and PhD degrees from Harvard University, and an MD from Harvard Medical School. He has been recognized with many awards, including the 2020 Swartz Prize in Theoretical and Computational Neuroscience, the 2018 Dickson Prize in Science, and an NIH Director’s Pioneer Award. Brown also served on President Barack Obama’s BRAIN Initiative Working Group. Among his many accomplishments, he has been cited for developing neural signal processing algorithms to characterize how neural systems represent and transmit information, and for unlocking the neurophysiology of how anesthetics produce the states of general anesthesia.

      Edelman says the process is underway to name a successor to Brown as co-director of HST at MIT. More

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      Using adversarial attacks to refine molecular energy predictions

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      Neural networks (NNs) are increasingly being used to predict new materials, the rate and yield of chemical reactions, and drug-target interactions, among others. For these applications, they are orders of magnitude faster than traditional methods such as quantum mechanical simulations. 

      The price for this agility, however, is reliability. Because machine learning models only interpolate, they may fail when used outside the domain of training data.

      But the part that worried Rafael Gómez-Bombarelli, the Jeffrey Cheah Career Development Professor in the MIT Department of Materials Science and Engineering, and graduate students Daniel Schwalbe-Koda and Aik Rui Tan was that establishing the limits of these machine learning (ML) models is tedious and labor-intensive. 

      This is particularly true for predicting ‘‘potential energy surfaces” (PES), or the map of a molecule’s energy in all its configurations. These surfaces encode the complexities of a molecule into flatlands, valleys, peaks, troughs, and ravines. The most stable configurations of a system are usually in the deep pits — quantum mechanical chasms from which atoms and molecules typically do not escape. 

      In a recent Nature Communications paper, the research team presented a way to demarcate the “safe zone” of a neural network by using “adversarial attacks.” Adversarial attacks have been studied for other classes of problems, such as image classification, but this is the first time that they are being used to sample molecular geometries in a PES. 

      “People have been using uncertainty for active learning for years in ML potentials. The key difference is that they need to run the full ML simulation and evaluate if the NN was reliable, and if it wasn’t, acquire more data, retrain and re-simulate. Meaning that it takes a long time to nail down the right model, and one has to run the ML simulation many times” explains Gómez-Bombarelli.

      The Gómez-Bombarelli lab at MIT works on a synergistic synthesis of first-principles simulation and machine learning that greatly speeds up this process. The actual simulations are run only for a small fraction of these molecules, and all those data are fed into a neural network that learns how to predict the same properties for the rest of the molecules. They have successfully demonstrated these methods for a growing class of novel materials that includes catalysts for producing hydrogen from water, cheaper polymer electrolytes for electric vehicles,  zeolites for molecular sieving, magnetic materials, and more. 

      The challenge, however, is that these neural networks are only as smart as the data they are trained on.  Considering the PES map, 99 percent of the data may fall into one pit, totally missing valleys that are of more interest. 

      Such wrong predictions can have disastrous consequences — think of a self-driving car that fails to identify a person crossing the street.

      One way to find out the uncertainty of a model is to run the same data through multiple versions of it. 

      For this project, the researchers had multiple neural networks predict the potential energy surface from the same data. Where the network is fairly sure of the prediction, the variation between the outputs of different networks is minimal and the surfaces largely converge. When the network is uncertain, the predictions of different models vary widely, producing a range of outputs, any of which could be the correct surface. 

      The spread in the predictions of a “committee of neural networks” is the “uncertainty” at that point. A good model should not just indicate the best prediction, but also indicates the uncertainty about each of these predictions. It’s like the neural network says “this property for material A will have a value of X and I’m highly confident about it.”

      This could have been an elegant solution but for the sheer scale of the combinatorial space. “Each simulation (which is ground feed for the neural network) may take from tens to thousands of CPU hours,” explains Schwalbe-Koda. For the results to be meaningful, multiple models must be run over a sufficient number of points in the PES, an extremely time-consuming process. 

      Instead, the new approach only samples data points from regions of low prediction confidence, corresponding to specific geometries of a molecule. These molecules are then stretched or deformed slightly so that the uncertainty of the neural network committee is maximized. Additional data are computed for these molecules through simulations and then added to the initial training pool. 

      The neural networks are trained again, and a new set of uncertainties are calculated. This process is repeated until the uncertainty associated with various points on the surface becomes well-defined and cannot be decreased any further. 

      Gómez-Bombarelli explains, “We aspire to have a model that is perfect in the regions we care about (i.e., the ones that the simulation will visit) without having had to run the full ML simulation, by making sure that we make it very good in high-likelihood regions where it isn’t.”

      The paper presents several examples of this approach, including predicting complex supramolecular interactions in zeolites. These materials are cavernous crystals that act as molecular sieves with high shape selectivity. They find applications in catalysis, gas separation, and ion exchange, among others.

      Because performing simulations of large zeolite structures is very costly, the researchers show how their method can provide significant savings in computational simulations. They used more than 15,000 examples to train a neural network to predict the potential energy surfaces for these systems. Despite the large cost required to generate the dataset, the final results are mediocre, with only around 80 percent of the neural network-based simulations being successful. To improve the performance of the model using traditional active learning methods, the researchers calculated an additional 5,000 data points, which improved the performance of the neural network potentials to 92 percent.

      However, when the adversarial approach is used to retrain the neural networks, the authors saw a performance jump to 97 percent using only 500 extra points. That’s a remarkable result, the researchers say, especially considering that each of these extra points takes hundreds of CPU hours. 

      This could be the most realistic method to probe the limits of models that researchers use to predict the behavior of materials and the progress of chemical reactions. More