The regulation was finalized in March 2024 but has never gone into effect. It would force public companies to disclose the risks that global warming poses to their operations.

The measure comes at a fraught time for the agency as President Donald Trump weighs its future.

A new report asked 65 companies about their concerns with buying carbon credits. Markets' lack of credibility was a theme.

Commission has yet to deliver an impact assessment it promised when announcing climate target.

The outbreak in Guangdong is the latest sign that tropical diseases are expanding their reach, as climate change lets mosquitoes live in new territories that have become warmer and wetter.

Wildfires have always affected the Mediterranean, but they have become a near-constant summer threat as climate change creates more extreme weather patterns.

The red palm weevil has devoured thousands of Uruguay's palm trees since its unexplained arrival from Southeast Asia in 2022.

In 2023, students from Calistoga Junior/Senior High School in California entered a year-long invention project run by the Lemelson-MIT Program. Tasked with finding problems to solve in their community, the students settled on an invention to keep firefighters and agricultural workers cool in hot working conditions.

Over the next 12 months, the students learned more about the problem from the workers, developed a prototype cooling system, and filed a patent for their invention. After presenting their solution at the program’s capstone Eurekafest event at MIT, the students were invited to the California State Capitol to share their work with lawmakers, and they went on to be selected as finalists in the student SXSW Innovation Awards.

For 20 years, the Lemelson-MIT InvenTeams Grant Initiative has inspired high school students across the country by supporting them through an extracurricular invention program that culminates in presentations on MIT’s campus each spring. The students select their own problems and invent their own solutions, receiving $7,500 in grants from Lemelson-MIT, along with mentorship, technical consultation, and more support to turn their ideas into reality.

In total, high school InvenTeams have been granted 19 U.S. patents since the program’s start, with many more teams, like the one from Calistoga, continuing work on their inventions after the program. Students often report an increased sense of confidence and interest in STEM subjects following their InvenTeams experience. In some cases, that new mindset changes students’ life trajectories.

“In a traditional school setting, students don’t always get the chance to show what they can do,” says Calistoga High School teacher Heather Brooks, who sponsored the 2023 team. “I was blown away by the students’ power and creativity.”

Turning students into inventors

The Lemelson Prize program started in 2004 with one $500,000 award given to a prolific inventor each year and smaller prizes given to inventor teams from MIT. In 2006, following a National Science Foundation report on the best ways to foster and support inventors, the program started awarding smaller grants to teams of high school students across the country.

“[Program founder] Jerome Lemelson wanted to inspire young people to become inventors and had a deep belief that America’s strength and innovation was driven by invention,” says Lemelson-MIT Executive Director Stephanie Couch. “He wanted young people to celebrate inventors like they celebrate rock stars and football players.”

When Couch arrived at MIT nine years ago, her research showed that giving small grants to younger students was the most successful way to increase students’ interest in STEM subjects.

Each year, the InvenTeams program receives between 50 and 80 applications from student teams across the country. From there, 20 to 30 teams are selected for Excite Awards. Those teams submit an in-depth application in which they describe the problem they’re solving, conduct patent research, and share early ideas for their solution. They also outline plans for community engagement, budget allocation, and additional background research.

Judges with a range of expertise select the finalists, who submit monthly updates throughout the year. Teams also meet with the community members they are inventing solutions for regularly.

“We see invention as a practice in empathy,” says Edwin Marrero, the interim invention education manager of the Lemelson-MIT program. “When you’re inventing, you’re inventing for somebody — and we like to say you’re inventing with somebody. Students learn to communicate and work in their communities. It’s a good skill to learn early in life.”

The final event at MIT, dubbed Eurekafest, is held every June. It features live presentations at the Stata Center that are open to the public and allow the students to showcase their inventions. Students stay in MIT dormitories for a few days leading up to the presentations and participate in a series of networking opportunities.

“The presentations are my favorite part, because people are peppering students with questions, and their depth of understanding, along with the confidence they project, is totally unlike anything you’ve ever seen from a high schooler,” Couch says.

This year’s teams presented ways to detect contamination in drinking water, help visually impaired people communicate, treat groundwater for use in agriculture, and more. Finalist teams hailed from Lubbock, Texas; Edison, New Jersey; Nitro, West Virginia, and — for the first time in the program’s history — MIT’s backyard of Cambridge, Massachusetts. The team from Cambridge invented a communication device for rowers on crew teams so they can hear from their coaches. They filed a patent for their invention.

“We’ve learned from our research that this one-year program really does transform the students’ perceptions of themselves, what they’re capable of, and what they’ll do next,” Couch says. “Also, by letting them pick what problem they want to solve and for whom they want to solve it, we’re giving them agency and tapping into that intrinsic motivation in life — to find meaning and purpose. How often in school do you get to find a problem versus being told which one to work on?”

Scaling invention education

There are many stories about the impact of the InvenTeam program on students. In 2016, a team of students on the autism spectrum developed a treadmill device and app to detect lameness in cows on dairy farms — a way to catch injury or disease in the animals. The students filed a patent for the device, which cost far less than other solutions on the market.

In 2018, a team from Garey High School in California developed a sensor device to help monitor foot health in diabetic patients and prevent amputations.

“Our school is one of the lowest-performing academically, and 99 percent of our students are low income,” says Antonio Gamboa, the school district’s former science department chair. “Before the Lemelson-MIT InvenTeams grant, district administrators said they didn’t have money to support science. Once they saw what these students could do, that turned around — not just in our school, but across the district.”

The InvenTeams program has been so successful the Lemelson-MIT program created a membership program, called Partners in Invention Education, to help many more schools adopt invention education. The curriculum stretches from kindergarten all the way to the first two years of college.

“As a middle school math teacher in New York City Public Schools, I noticed kids are falling out of love with these STEM subjects at an early age,” Marrero says. “I think a big reason for that is it’s not taught in a way that meaningful to them. There often aren’t real-world applications in lessons. Lemelson-MIT’s invention education makes STEM subjects relevant to kids. They’re the drivers of the learning. They might discover they need math or science skills to solve the problem they’re working on, and it creates a different level of motivation.”

Nature Climate Change, Published online: 25 July 2025; doi:10.1038/s41558-025-02379-x

The authors use a machine learning approach and in situ pigment samples to identify summer shifts (1997–2023) in the abundance and composition of Antarctic phytoplankton. While smaller phytoplankton groups generally increased, diatom chlorophyll a broadly decreased, with putative impacts on food webs and the carbon sink.

Gevorg Minasyan MAP ’23 first discovered the MITx MicroMasters Program in Data, Economics, and Design of Policy (DEDP) — jointly led by the Abdul Latif Jameel Poverty Action Lab (J-PAL) and MIT Open Learning — when he was looking to better understand the process of building effective, evidence-based policies while working at the Central Bank of Armenia. After completing the MicroMasters program, Minasyan was inspired to pursue MIT’s Master’s in Data, Economics, and Design of Policy program.

Today, Minasyan is the director of the Research and Training Center at the Central Bank of Armenia. He has not only been able to apply what he has learned at MIT to his work, but he has also sought to institutionalize a culture of evidence-based policymaking at the bank and more broadly in Armenia. He spoke with MIT Open Learning about his journey through the DEDP programs, key takeaways, and how what he learned at MIT continues to guide his work.

Q: What initially drew you to the DEDP MicroMasters, and what were some highlights of the program?

A: Working at the Central Bank of Armenia, I was constantly asking myself: Can we build a system in which public policy decisions are grounded in rigorous evidence? Too often, I observed public programs that were well-intentioned and seemed to address pressing challenges, but ultimately failed to bring tangible change. Sometimes it was due to flawed design; other times, the goals simply didn’t align with what the public actually needed or expected. These experiences left a deep impression on me and sparked a strong desire to better understand what works, what doesn’t, and why.

That search led me to the DEDP MicroMasters program, which turned out to be a pivotal step in my professional journey. From the very first course, I realized that this was not just another academic program — it was a completely new way of thinking about development policy. The courses combined rigorous training in economics, data analysis, and impact evaluation with a strong emphasis on practical application. We weren’t just learning formulas or running regressions — we were being trained to ask the right questions, to think critically about causality, and to understand the trade-offs of policy choices.

Another aspect that set the MicroMasters apart was its blended structure. I was able to pursue a globally top-tier education while continuing my full-time responsibilities at the Central Bank. This made the learning deeply relevant and immediately applicable. Even as I was studying, I found myself incorporating insights from class into my day-to-day policy work, whether it was refining how we evaluated financial inclusion programs or rethinking the way we analyzed administrative data.

At the same time, the global nature of the program created a vibrant, diverse community. I engaged with students and professionals from dozens of countries, each bringing different perspectives. These interactions enriched the coursework and helped me to realize that despite the differences in context, the challenges of effective policy design — and the power of evidence to improve lives — were remarkably universal. It was a rare combination: intellectually rigorous, practically grounded, globally connected, and personally transformative.

Q: Can you describe your experiences in the Master’s in Data, Economics, and Design of Policy residential program?

A: The MicroMasters experience inspired me to go further, and I decided to apply for the full-time, residential master’s at MIT. That year was nothing short of transformative. It not only sharpened my technical and analytical skills, but also fundamentally changed the way I think about policymaking.

One of the most influential courses I took during the master’s program was 14.760 (Firms, Markets, Trade, and Growth). The analytical tools it provided mapped directly onto the systemic challenges I saw among Armenian firms. Motivated by this connection, I developed a similar course, which I now teach at the American University of Armenia. Each year, I work with students to investigate the everyday constraints that hinder firm performance, with the ultimate goal of producing data-driven research that could inform business strategy in Armenia.

The residential master’s program taught me that evidence-based decision-making starts with a mindset shift. It’s not just about applying tools, it’s about being open to questioning assumptions, being transparent about uncertainty, and being humble enough to let data challenge intuition. I also came to appreciate that truly effective policy design isn’t about finding one-off solutions, but about creating dynamic feedback loops that allow us to continuously learn from implementation.

This is essential to refining programs in real time, adapting to new information, and avoiding the trap of static, one-size-fits-all approaches. Equally valuable was becoming part of the MIT and J-PAL’s global network. The relationships I built with researchers, practitioners, and fellow students from around the world gave me lasting insights into how institutions can systematically embed analysis in their core operations. This exposure helped me to see the possibilities not just for my own work, but for how public institutions like central banks can lead the way in advancing an evidence-based culture.

Q: How are you applying what you’ve learned in the DEDP programs to the Central Bank of Armenia?

A: As director of the Research and Training Center at the Central Bank of Armenia, I have taken on a new kind of responsibility: leading the effort to scale evidence-based decision-making not only within the Central Bank, but across a broader ecosystem of public institutions in Armenia. This means building internal capacity, rethinking how research informs policy, and fostering partnerships that promote a culture of data-driven decision-making.

Beyond the classroom, the skills I developed through the DEDP program have been critical to my role in shaping real-world policy in Armenia. A particularly timely example is our national push toward a cashless economy — one of the most prominent and complex reform agendas today. In recent years, the government has rolled out a suite of bold policies aimed at boosting the adoption of non-cash payments, all part of a larger vision to modernize the financial system, reduce the shadow economy, and increase transparency. Key initiatives include a cashback program designed to encourage pensioners to use digital payments and the mandatory installation of non-cash payment terminals across businesses nationwide. In my role on an inter-agency policy team, I rely heavily on the analytical tools from DEDP to evaluate these policies and propose regulatory adjustments to ensure the transition is not only effective, but also inclusive and sustainable.

The Central Bank of Armenia recently collaborated with J-PAL Europe to co-design and host a policy design and evaluation workshop. The workshop brought together policymakers, central bankers, and analysts from various sectors and focused on integrating evidence throughout the policy cycle, from defining the problem to designing interventions and conducting rigorous evaluations. It’s just the beginning, but it already reflects how the ideas, tools, and values I absorbed at MIT are now taking institutional form back home.

Our ultimate goal is to institutionalize the use of policy evaluation as a standard practice — not as an occasional activity, but as a core part of how we govern. We’re working to embed a stronger feedback culture in policymaking, one that prioritizes learning before scaling. More experimentation, piloting, and iteration are essential before committing to large-scale rollouts of public programs. This shift requires patience and persistence, but it is critical if we want policies that are not only well-designed, but also effective, inclusive, and responsive to people’s needs.

Looking ahead, I remain committed to advancing this transformation, by building the systems, skills, and partnerships that can sustain evidence-based policymaking in Armenia for the long term. 

In an office at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), a soft robotic hand carefully curls its fingers to grasp a small object. The intriguing part isn’t the mechanical design or embedded sensors — in fact, the hand contains none. Instead, the entire system relies on a single camera that watches the robot’s movements and uses that visual data to control it.

This capability comes from a new system CSAIL scientists developed, offering a different perspective on robotic control. Rather than using hand-designed models or complex sensor arrays, it allows robots to learn how their bodies respond to control commands, solely through vision. The approach, called Neural Jacobian Fields (NJF), gives robots a kind of bodily self-awareness. An open-access paper about the work was published in Nature on June 25.

“This work points to a shift from programming robots to teaching robots,” says Sizhe Lester Li, MIT PhD student in electrical engineering and computer science, CSAIL affiliate, and lead researcher on the work. “Today, many robotics tasks require extensive engineering and coding. In the future, we envision showing a robot what to do, and letting it learn how to achieve the goal autonomously.”

The motivation stems from a simple but powerful reframing: The main barrier to affordable, flexible robotics isn't hardware — it’s control of capability, which could be achieved in multiple ways. Traditional robots are built to be rigid and sensor-rich, making it easier to construct a digital twin, a precise mathematical replica used for control. But when a robot is soft, deformable, or irregularly shaped, those assumptions fall apart. Rather than forcing robots to match our models, NJF flips the script — giving robots the ability to learn their own internal model from observation.

Look and learn

This decoupling of modeling and hardware design could significantly expand the design space for robotics. In soft and bio-inspired robots, designers often embed sensors or reinforce parts of the structure just to make modeling feasible. NJF lifts that constraint. The system doesn’t need onboard sensors or design tweaks to make control possible. Designers are freer to explore unconventional, unconstrained morphologies without worrying about whether they’ll be able to model or control them later.

“Think about how you learn to control your fingers: you wiggle, you observe, you adapt,” says Li. “That’s what our system does. It experiments with random actions and figures out which controls move which parts of the robot.”

The system has proven robust across a range of robot types. The team tested NJF on a pneumatic soft robotic hand capable of pinching and grasping, a rigid Allegro hand, a 3D-printed robotic arm, and even a rotating platform with no embedded sensors. In every case, the system learned both the robot’s shape and how it responded to control signals, just from vision and random motion.

The researchers see potential far beyond the lab. Robots equipped with NJF could one day perform agricultural tasks with centimeter-level localization accuracy, operate on construction sites without elaborate sensor arrays, or navigate dynamic environments where traditional methods break down.

At the core of NJF is a neural network that captures two intertwined aspects of a robot’s embodiment: its three-dimensional geometry and its sensitivity to control inputs. The system builds on neural radiance fields (NeRF), a technique that reconstructs 3D scenes from images by mapping spatial coordinates to color and density values. NJF extends this approach by learning not only the robot’s shape, but also a Jacobian field, a function that predicts how any point on the robot’s body moves in response to motor commands.

To train the model, the robot performs random motions while multiple cameras record the outcomes. No human supervision or prior knowledge of the robot’s structure is required — the system simply infers the relationship between control signals and motion by watching.

Once training is complete, the robot only needs a single monocular camera for real-time closed-loop control, running at about 12 Hertz. This allows it to continuously observe itself, plan, and act responsively. That speed makes NJF more viable than many physics-based simulators for soft robots, which are often too computationally intensive for real-time use.

In early simulations, even simple 2D fingers and sliders were able to learn this mapping using just a few examples. By modeling how specific points deform or shift in response to action, NJF builds a dense map of controllability. That internal model allows it to generalize motion across the robot’s body, even when the data are noisy or incomplete.

“What’s really interesting is that the system figures out on its own which motors control which parts of the robot,” says Li. “This isn’t programmed — it emerges naturally through learning, much like a person discovering the buttons on a new device.”

The future is soft

For decades, robotics has favored rigid, easily modeled machines — like the industrial arms found in factories — because their properties simplify control. But the field has been moving toward soft, bio-inspired robots that can adapt to the real world more fluidly. The trade-off? These robots are harder to model.

“Robotics today often feels out of reach because of costly sensors and complex programming. Our goal with Neural Jacobian Fields is to lower the barrier, making robotics affordable, adaptable, and accessible to more people. Vision is a resilient, reliable sensor,” says senior author and MIT Assistant Professor Vincent Sitzmann, who leads the Scene Representation group. “It opens the door to robots that can operate in messy, unstructured environments, from farms to construction sites, without expensive infrastructure.”

“Vision alone can provide the cues needed for localization and control — eliminating the need for GPS, external tracking systems, or complex onboard sensors. This opens the door to robust, adaptive behavior in unstructured environments, from drones navigating indoors or underground without maps to mobile manipulators working in cluttered homes or warehouses, and even legged robots traversing uneven terrain,” says co-author Daniela Rus, MIT professor of electrical engineering and computer science and director of CSAIL. “By learning from visual feedback, these systems develop internal models of their own motion and dynamics, enabling flexible, self-supervised operation where traditional localization methods would fail.”

While training NJF currently requires multiple cameras and must be redone for each robot, the researchers are already imagining a more accessible version. In the future, hobbyists could record a robot’s random movements with their phone, much like you’d take a video of a rental car before driving off, and use that footage to create a control model, with no prior knowledge or special equipment required.

The system doesn’t yet generalize across different robots, and it lacks force or tactile sensing, limiting its effectiveness on contact-rich tasks. But the team is exploring new ways to address these limitations: improving generalization, handling occlusions, and extending the model’s ability to reason over longer spatial and temporal horizons.

“Just as humans develop an intuitive understanding of how their bodies move and respond to commands, NJF gives robots that kind of embodied self-awareness through vision alone,” says Li. “This understanding is a foundation for flexible manipulation and control in real-world environments. Our work, essentially, reflects a broader trend in robotics: moving away from manually programming detailed models toward teaching robots through observation and interaction.”

This paper brought together the computer vision and self-supervised learning work from the Sitzmann lab and the expertise in soft robots from the Rus lab. Li, Sitzmann, and Rus co-authored the paper with CSAIL affiliates Annan Zhang SM ’22, a PhD student in electrical engineering and computer science (EECS); Boyuan Chen, a PhD student in EECS; Hanna Matusik, an undergraduate researcher in mechanical engineering; and Chao Liu, a postdoc in the Senseable City Lab at MIT. 

The research was supported by the Solomon Buchsbaum Research Fund through MIT’s Research Support Committee, an MIT Presidential Fellowship, the National Science Foundation, and the Gwangju Institute of Science and Technology.

City life is often described as “fast-paced.” A new study suggests that’s more true that ever.

The research, co-authored by MIT scholars, shows that the average walking speed of pedestrians in three northeastern U.S. cities increased 15 percent from 1980 to 2010. The number of people lingering in public spaces declined by 14 percent in that time as well.

The researchers used machine-learning tools to assess 1980s-era video footage captured by renowned urbanist William Whyte, in Boston, New York, and Philadelphia. They compared the old material with newer videos from the same locations.

“Something has changed over the past 40 years,” says MIT professor of the practice Carlo Ratti, a co-author of the new study. “How fast we walk, how people meet in public space — what we’re seeing here is that public spaces are working in somewhat different ways, more as a thoroughfare and less a space of encounter.”

The paper, “Exploring the social life of urban spaces through AI,” is published this week in the Proceedings of the National Academy of Sciences. The co-authors are Arianna Salazar-Miranda MCP ’16, PhD ’23, an assistant professor at Yale University’s School of the Environment; Zhuanguan Fan of the University of Hong Kong; Michael Baick; Keith N. Hampton, a professor at Michigan State University; Fabio Duarte, associate director of the Senseable City Lab; Becky P.Y. Loo of the University of Hong Kong; Edward Glaeser, the Fred and Eleanor Glimp Professor of Economics at Harvard University; and Ratti, who is also director of MIT’s Senseable City Lab.

The results could help inform urban planning, as designers seek to create new public areas or modify existing ones.

“Public space is such an important element of civic life, and today partly because it counteracts the polarization of digital space,” says Salazar-Miranda. “The more we can keep improving public space, the more we can make our cities suited for convening.”

Meet you at the Met

Whyte was a prominent social thinker whose famous 1956 book, “The Organization Man,” probing the apparent culture of corporate conformity in the U.S., became a touchstone of its decade.

However, Whyte spent the latter decades of his career focused on urbanism. The footage he filmed, from 1978 through 1980, was archived by a Brooklyn-based nonprofit organization called the Project for Public Spaces and later digitized by Hampton and his students.

Whyte chose to make his recording at four spots in the three cities combined: Boston’s Downtown Crossing area; New York City’s Bryant Park; the steps of the Metropolitan Museum of Art in New York, a famous gathering point and people-watching spot; and Philadelphia’s Chestnut Street.

In 2010, a group led by Hampton then shot new footage at those locations, at the same times of day Whyte had, to compare and contrast current-day dynamics with those of Whyte’s time. To conduct the study, the co-authors used computer vision and AI models to summarize and quantify the activity in the videos.

The researchers have found that some things have not changed greatly. The percentage of people walking alone barely moved, from 67 percent in 1980 to 68 percent in 2010. On the other hand, the percentage of individuals entering these public spaces who became part of a group declined a bit. In 1980, 5.5 percent of the people approaching these spots met up with a group; in 2010, that was down to 2 percent.

“Perhaps there’s a more transactional nature to public space today,” Ratti says.

Fewer outdoor groups: Anomie or Starbucks?

If people’s behavioral patterns have altered since 1980, it’s natural to ask why. Certainly some of the visible changes seem consistent with the pervasive use of cellphones; people organize their social lives by phone now, and perhaps zip around more quickly from place to place as a result.

“When you look at the footage from William Whyte, the people in public spaces were looking at each other more,” Ratti says. “It was a place you could start a conversation or run into a friend. You couldn’t do things online then. Today, behavior is more predicated on texting first, to meet in public space.”

As the scholars note, if groups of people hang out together slightly less often in public spaces, there could be still another reason for that: Starbucks and its competitors. As the paper states, outdoor group socializing may be less common due to “the proliferation of coffee shops and other indoor venues. Instead of lingering on sidewalks, people may have moved their social interactions into air-conditioned, more comfortable private spaces.”

Certainly coffeeshops were far less common in big cities in 1980, and the big chain coffeeshops did not exist.

On the other hand, public-space behavior might have been evolving all this time regardless of Starbucks and the like. The researchers say the new study offers a proof-of-concept for its method and has encouraged them to conduct additional work. Ratti, Duarte, and other researchers from MIT’s Senseable City Lab have turned their attention to an extensive survey of European public spaces in an attempt to shed more light on the interaction between people and the public form.

“We are collecting footage from 40 squares in Europe,” Duarte says. “The question is: How can we learn at a larger scale? This is in part what we’re doing.” 

One of the shared, fundamental goals of most chemistry researchers is the need to predict a molecule’s properties, such as its boiling or melting point. Once researchers can pinpoint that prediction, they’re able to move forward with their work yielding discoveries that lead to medicines, materials, and more. Historically, however, the traditional methods of unveiling these predictions are associated with a significant cost — expending time and wear and tear on equipment, in addition to funds.

Enter a branch of artificial intelligence known as machine learning (ML). ML has lessened the burden of molecule property prediction to a degree, but the advanced tools that most effectively expedite the process — by learning from existing data to make rapid predictions for new molecules — require the user to have a significant level of programming expertise. This creates an accessibility barrier for many chemists, who may not have the significant computational proficiency required to navigate the prediction pipeline. 

To alleviate this challenge, researchers in the McGuire Research Group at MIT have created ChemXploreML, a user-friendly desktop app that helps chemists make these critical predictions without requiring advanced programming skills. Freely available, easy to download, and functional on mainstream platforms, this app is also built to operate entirely offline, which helps keep research data proprietary. The exciting new technology is outlined in an article published recently in the Journal of Chemical Information and Modeling.

One specific hurdle in chemical machine learning is translating molecular structures into a numerical language that computers can understand. ChemXploreML automates this complex process with powerful, built-in "molecular embedders" that transform chemical structures into informative numerical vectors. Next, the software implements state-of-the-art algorithms to identify patterns and accurately predict molecular properties like boiling and melting points, all through an intuitive, interactive graphical interface. 

"The goal of ChemXploreML is to democratize the use of machine learning in the chemical sciences,” says Aravindh Nivas Marimuthu, a postdoc in the McGuire Group and lead author of the article. “By creating an intuitive, powerful, and offline-capable desktop application, we are putting state-of-the-art predictive modeling directly into the hands of chemists, regardless of their programming background. This work not only accelerates the search for new drugs and materials by making the screening process faster and cheaper, but its flexible design also opens doors for future innovations.” 

ChemXploreML is designed to to evolve over time, so as future techniques and algorithms are developed, they can be seamlessly integrated into the app, ensuring that researchers are always able to access and implement the most up-to-date methods. The application was tested on five key molecular properties of organic compounds — melting point, boiling point, vapor pressure, critical temperature, and critical pressure — and achieved high accuracy scores of up to 93 percent for the critical temperature. The researchers also demonstrated that a new, more compact method of representing molecules (VICGAE) was nearly as accurate as standard methods, such as Mol2Vec, but was up to 10 times faster.

“We envision a future where any researcher can easily customize and apply machine learning to solve unique challenges, from developing sustainable materials to exploring the complex chemistry of interstellar space,” says Marimuthu. Joining him on the paper is senior author and Class of 1943 Career Development Assistant Professor of Chemistry Brett McGuire.

The current state of digital identity is a mess. Your personal information is scattered across hundreds of locations: social media companies, IoT companies, government agencies, websites you have accounts on, and data brokers you’ve never heard of. These entities collect, store, and trade your data, often without your knowledge or consent. It’s both redundant and inconsistent. You have hundreds, maybe thousands, of fragmented digital profiles that often contain contradictory or logically impossible information. Each serves its own purpose, yet there is no central override and control to serve you—as the identity owner...

Earnings calls Wednesday revealed how the biggest wind and solar companies are confronting the president’s hostility toward their industry.

The agency appears poised to tie its deregulatory agenda to undoing the 2009 scientific finding behind most climate rules.

Top Republican legislators said pointing fingers would undermine efforts to improve the state's future responses.

House lawmakers are planning to introduce bipartisan legislation to overhaul the agency.

Though the decision is nonbinding, the ruling from the International Court of Justice could open the door for more litigation against corporate polluters.

Ernesto Apreza will work on getting corporate commitments for net zero.