More than 1,500 parents and children across the Atlanta metropolitan area attended a jam-packed second annual Georgia Tech Science and Engineering Day held on Saturday, March 11 in conjunction with the tenth annual 2023 Atlanta Science Festival. Located across five campus buildings, more than 40 demonstrations, hands-on STEAM activities, tours, and learning opportunities designed to engage and educate participants were offered by students, staff, and faculty volunteers.

Some of this year’s demonstration topics included battery fuel cells, nanotechnology, DNA, immunoengineering, chemistry, engineering, superconductivity levitation, wastewater treatment, aerospace, space outreach, virtual reality, biology, robotics, computing, 3D printing, paper making, and much more.

A parent attending from Peachtree City said, “we’ve discovered our son has an affinity for math and science. He’s handling tenth grade science level coursework, yet he’s only in the seventh grade and can understand math formulas ahead of his age group. We brought him here to expose him to a variety of technologies and advanced equipment that he won’t see or be exposed to in his middle school. The staff and professors here have been very kind to show him how to use some of the equipment we’ve seen. And his eyes have gotten bigger all day because of these interactions.”

Virginia Howell, director of the Roberts C. Williams Museum of Paper Making in the Renewable Bioproducts Institute at Georgia Tech said, “the paper museum is delighted to be part of the Georgia Tech Science and Engineering Day. It's a great opportunity for people to learn more about the paper museum and get hands-on experience in making a sheet of paper to take home. We offer workshops, classes, and tours to students across the state of Georgia. Kids have been lined up all day to participate at our tables today.”

Demonstrations included how to extract DNA, seeing LIDAR in action, experiencing heat sensing sensors, how x-rays are used, viewing scanning electron microscopes, playing a virtual reality game, experiencing chemical reactions, watching 3D printing, making slime, showing atom-level nano materials in synthesized materials, neuroscience demos, liquid nitrogen experiments, and many more.

Presentation areas were hosted by the Institute for Electronics and Nanotechnology, the Institute for Robotics and Intelligent Machines, and the Institute for Bioengineering and Biosciences who provided valuable space in their buildings to house demonstrations. The Ford Environmental Science & Technology Building and Molecular Science & Engineering Building also donated space for demonstrations.

Another tour offering during Science and Engineering Day was the Flowers Invention Studio at Georgia Tech which offers more than 5,000 square feet of industrial makerspace equipment.

“We are the largest student-run maker space in the nation,” said Lillian Tso, president of the Invention Studio and a fourth-year mechanical engineering student. “We house industrial grade equipment for prototyping and manufacturing—we support anything that students want to build. We're open for all students of all majors of all years. They can use our equipment for free which includes CNC machines, more than 50 3D printers, waterjets, laser cutters, and many other professional-level tools. This is our first year participating in the Georgia Tech Science and Engineering Day. We wanted to do a lot more outreach to the Georgia Tech campus and the greater Atlanta community."

Lucas Garza, president-elect of the Invention Studio, added, “we’ve had a busy day offering tours of our studio throughout the festival.”

Located in the mezzanine of the Marcus Nanotechnology Building, Ethan Sirak, a fourth-year aerospace student with the Georgia Space Grant Consortium, was providing kids with exposure to space facts and allowing them to perform crafts related to planets and space. The consortium is an organization under NASA which aims to promote STEM exposure to kids of all ages. He also assists with the Aerospace Engineering Outreach Program. He was partnered at his hands-on learning table with Bill McNutt Jr., a senior aerospace engineering student.

A young participant from Suwanee, Georgia, said, “I want to go to school at Georgia Tech because of aerospace engineering. I want to go on good adventures in future space flight and design things.”

His mom, a sixth-grade science teacher added, “I love coming to science fairs to get new ideas for my students and I love to bring my family because we just have a great time. This is our very first science fair here at Georgia Tech. We've been to ones in north Georgia because that's pretty close to where we live. But when we saw this was available, we're like, yeah, we're coming down to Tech for this today—and having a great time.”

While attendees were able to get a peek into one of the nation’s most research-intensive universities, the event also allowed the many researchers and students participating the opportunity to share their science and engineering work with the public.

One of the more unique tables was manned by Alison Reynolds, an instruction archivist with research services in the Georgia Tech library. She was displaying a selection of unique items from Georgia Tech’s science fiction archives and special collections. She said, “we’ve been teaching with science fiction since 1971 and our collection is now one of the largest science fiction collections in the United States. We wanted to display part of our special collection.”

“I had several Georgia school systems reach out to me that were interested in attending this event,“ said Leslie O 'Neill, education outreach manager with the Southeastern Nanotechnology Infrastructure Corridor (SENIC) at Georgia Tech. “Georgia Tech plays a vital part in its community. We wanted to showcase the campus; the student, faculty and staff research; and the amazing science and engineering being done. We’ve had a fantastic turnout this year for this event.”
 

- Written by Benjamin Wright -

As Georgia Tech establishes itself as a national leader in AI research and education, some researchers on campus are putting AI to work to help meet sustainability goals in a range of areas including climate change adaptation and mitigation, urban farming, food distribution, and life cycle assessments while also focusing on ways to make sure AI is used ethically.

Josiah Hester, interim associate director for Community-Engaged Research in the Brook Byers Institute for Sustainable Systems (BBISS) and associate professor in the School of Interactive Computing, sees these projects as wins from both a research standpoint and for the local, national, and global communities they could affect.

“These faculty exemplify Georgia Tech's commitment to serving and partnering with communities in our research,” he says. “Sustainability is one of the most pressing issues of our time. AI gives us new tools to build more resilient communities, but the complexities and nuances in applying this emerging suite of technologies can only be solved by community members and researchers working closely together to bridge the gap. This approach to AI for sustainability strengthens the bonds between our university and our communities and makes lasting impacts due to community buy-in.”

Flood Monitoring and Carbon Storage

Peng Chen, assistant professor in the School of Computational Science and Engineering in the College of Computing, focuses on computational mathematics, data science, scientific machine learning, and parallel computing. Chen is combining these areas of expertise to develop algorithms to assist in practical applications such as flood monitoring and carbon dioxide capture and storage.

He is currently working on a National Science Foundation (NSF) project with colleagues in Georgia Tech’s School of City and Regional Planning and from the University of South Florida to develop flood models in the St. Petersburg, Florida area. As a low-lying state with more than 8,400 miles of coastline, Florida is one of the states most at risk from sea level rise and flooding caused by extreme weather events sparked by climate change.

Chen’s novel approach to flood monitoring takes existing high-resolution hydrological and hydrographical mapping and uses machine learning to incorporate real-time updates from social media users and existing traffic cameras to run rapid, low-cost simulations using deep neural networks. Current flood monitoring software is resource and time-intensive. Chen’s goal is to produce live modeling that can be used to warn residents and allocate emergency response resources as conditions change. That information would be available to the general public through a portal his team is working on.

“This project focuses on one particular community in Florida,” Chen says, “but we hope this methodology will be transferable to other locations and situations affected by climate change.”

In addition to the flood-monitoring project in Florida, Chen and his colleagues are developing new methods to improve the reliability and cost-effectiveness of storing carbon dioxide in underground rock formations. The process is plagued with uncertainty about the porosity of the bedrock, the optimal distribution of monitoring wells, and the rate at which carbon dioxide can be injected without over-pressurizing the bedrock, leading to collapse. The new simulations are fast, inexpensive, and minimize the risk of failure, which also decreases the cost of construction.

“Traditional high-fidelity simulation using supercomputers takes hours and lots of resources,” says Chen. “Now we can run these simulations in under one minute using AI models without sacrificing accuracy. Even when you factor in AI training costs, this is a huge savings in time and financial resources.”

Flood monitoring and carbon capture are passion projects for Chen, who sees an opportunity to use artificial intelligence to increase the pace and decrease the cost of problem-solving.

“I’m very excited about the possibility of solving grand challenges in the sustainability area with AI and machine learning models,” he says. “Engineering problems are full of uncertainty, but by using this technology, we can characterize the uncertainty in new ways and propagate it throughout our predictions to optimize designs and maximize performance.”

Urban Farming and Optimization

Yongsheng Chen works at the intersection of food, energy, and water. As the Bonnie W. and Charles W. Moorman Professor in the School of Civil and Environmental Engineering and director of the Nutrients, Energy, and Water Center for Agriculture Technology, Chen is focused on making urban agriculture technologically feasible, financially viable, and, most importantly, sustainable. To do that he’s leveraging AI to speed up the design process and optimize farming and harvesting operations.

Chen’s closed-loop hydroponic system uses anaerobically treated wastewater for fertilization and irrigation by extracting and repurposing nutrients as fertilizer before filtering the water through polymeric membranes with nano-scale pores. Advancing filtration and purification processes depends on finding the right membrane materials to selectively separate contaminants, including antibiotics and per- and polyfluoroalkyl substances (PFAS). Chen and his team are using AI and machine learning to guide membrane material selection and fabrication to make contaminant separation as efficient as possible. Similarly, AI and machine learning are assisting in developing carbon capture materials such as ionic liquids that can retain carbon dioxide generated during wastewater treatment and redirect it to hydroponics systems, boosting food productivity.

“A fundamental angle of our research is that we do not see municipal wastewater as waste,” explains Chen. “It is a resource we can treat and recover components from to supply irrigation, fertilizer, and biogas, all while reducing the amount of energy used in conventional wastewater treatment methods.”

In addition to aiding in materials development, which reduces design time and production costs, Chen is using machine learning to optimize the growing cycle of produce, maximizing nutritional value. His USDA-funded vertical farm uses autonomous robots to measure critical cultivation parameters and take pictures without destroying plants. This data helps determine optimum environmental conditions, fertilizer supply, and harvest timing, resulting in a faster-growing, optimally nutritious plant with less fertilizer waste and lower emissions.

Chen’s work has received considerable federal funding. As the Urban Resilience and Sustainability Thrust Leader within the NSF-funded AI Institute for Advances in Optimization (AI4OPT), he has received additional funding to foster international collaboration in digital agriculture with colleagues across the United States and in Japan, Australia, and India.

Optimizing Food Distribution

At the other end of the agricultural spectrum is postdoc Rosemarie Santa González in the H. Milton Stewart School of Industrial and Systems Engineering, who is conducting her research under the supervision of Professor Chelsea White and Professor Pascal Van Hentenryck, the director of Georgia Tech’s AI Hub as well as the director of AI4OPT.

Santa González is working with the Wisconsin Food Hub Cooperative to help traditional farmers get their products into the hands of consumers as efficiently as possible to reduce hunger and food waste. Preventing food waste is a priority for both the EPA and USDA. Current estimates are that 30 to 40% of the food produced in the United States ends up in landfills, which is a waste of resources on both the production end in the form of land, water, and chemical use, as well as a waste of resources when it comes to disposing of it, not to mention the impact of the greenhouses gases when wasted food decays.

To tackle this problem, Santa González and the Wisconsin Food Hub are helping small-scale farmers access refrigeration facilities and distribution chains. As part of her research, she is helping to develop AI tools that can optimize the logistics of the small-scale farmer supply chain while also making local consumers in underserved areas aware of what’s available so food doesn’t end up in landfills.

“This solution has to be accessible,” she says. “Not just in the sense that the food is accessible, but that the tools we are providing to them are accessible. The end users have to understand the tools and be able to use them. It has to be sustainable as a resource.”

Making AI accessible to people in the community is a core goal of the NSF’s AI Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE), one of the partners involved with the project.

“A large segment of the population we are working with, which includes historically marginalized communities, has a negative reaction to AI. They think of machines taking over, or data being stolen. Our goal is to democratize AI in these decision-support tools as we work toward the UN Sustainable Development Goal of Zero Hunger. There is so much power in these tools to solve complex problems that have very real results. More people will be fed and less food will spoil before it gets to people’s homes.”

Santa González hopes the tools they are building can be packaged and customized for food co-ops everywhere.

AI and Ethics

Like Santa González, Joe Bozeman III is also focused on the ethical and sustainable deployment of AI and machine learning, especially among marginalized communities. The assistant professor in the School of Civil and Environmental Engineering is an industrial ecologist committed to fostering ethical climate change adaptation and mitigation strategies. His SEEEL Lab works to make sure researchers understand the consequences of decisions before they move from academic concepts to policy decisions, particularly those that rely on data sets involving people and communities.

“With the administration of big data, there is a human tendency to assume that more data means everything is being captured, but that's not necessarily true,” he cautions. “More data could mean we're just capturing more of the data that already exists, while new research shows that we’re not including information from marginalized communities that have historically not been brought into the decision-making process. That includes underrepresented minorities, rural populations, people with disabilities, and neurodivergent people who may not interface with data collection tools.”

Bozeman is concerned that overlooking marginalized communities in data sets will result in decisions that at best ignore them and at worst cause them direct harm.

“Our lab doesn't wait for the negative harms to occur before we start talking about them,” explains Bozeman, who holds a courtesy appointment in the School of Public Policy. “Our lab forecasts what those harms will be so decision-makers and engineers can develop technologies that consider these things.”

He focuses on urbanization, the food-energy-water nexus, and the circular economy. He has found that much of the research in those areas is conducted in a vacuum without consideration for human engagement and the impact it could have when implemented.

Bozeman is lobbying for built-in tools and safeguards to mitigate the potential for harm from researchers using AI without appropriate consideration. He already sees a disconnect between the academic world and the public. Bridging that trust gap will require ethical uses of AI.

“We have to start rigorously including their voices in our decision-making to begin gaining trust with the public again. And with that trust, we can all start moving toward sustainable development. If we don't do that, I don't care how good our engineering solutions are, we're going to miss the boat entirely on bringing along the majority of the population.”

BBISS Support

Moving forward, Hester is excited about the impact the Brooks Byers Institute for Sustainable Systems can have on AI and sustainability research through a variety of support mechanisms.

“BBISS continues to invest in faculty development and training in community-driven research strategies, including the Community Engagement Faculty Fellows Program (with the Center for Sustainable Communities Research and Education), while empowering multidisciplinary teams to work together to solve grand engineering challenges with AI by supporting the AI+Climate Faculty Interest Group, as well as partnering with and providing administrative support for community-driven research projects.”

 

 

A new agreement between Los Alamos National Laboratory (LANL) and the National Science Foundation’s Artificial Intelligence Institute for Advances in Optimization (AI4OPT) at Georgia Tech is set to propel research in applied artificial intelligence (AI) and engage students and professionals in this rapidly growing field.

“This collaboration will help develop new AI technologies for the next generation of scientific discovery and the design of complex systems and the control of engineered systems,” said Russell Bent, scientist at Los Alamos. “At Los Alamos, we have a lot of interest in optimizing complex systems. We see an opportunity with AI to enhance system resilience and efficiency in the face of climate change, extreme events, and other challenges.”

The agreement establishes a research and educational partnership focused on advancing AI tools for a next-generation power grid. Maintaining and optimizing the energy grid involves extensive computation, and AI-informed approaches, including modeling, could address power-grid issues more effectively.

AI Approaches to Optimization and Problem-Solving

Optimization involves finding solutions that utilize resources effectively and efficiently. This research partnership will leverage Georgia Tech's expertise to develop “trustworthy foundation models” that, by incorporating AI, reduce the vast computing resources needed for solving complex problems.

In energy grid systems, optimization involves quickly sorting through possibilities and resources to deliver immediate solutions during a power-distribution crisis. The research will develop “optimization proxies” that extend current methods by incorporating broader parameters such as generator limits, line ratings, and grid topologies. Training these proxies with AI for energy applications presents a significant research challenge.

The collaboration will also address problems related to LANL’s diverse missions and applications. The team’s research will advance pioneering efforts in graph-based, physics-informed machine learning to solve Laboratory mission problems.

Outreach and Training Opportunities

In January 2025, the Laboratory will host a Grid Science Winter School and Conference, featuring lectures from LANL scientists and academic partners on electrical grid methods and techniques. With Georgia Tech as a co-organizer, AI optimization for the energy grid will be a focal point of the event.

Since 2020, the Laboratory has been working with Georgia Tech on energy grid projects. AI4OPT, which includes several industrial and academic partners, aims to achieve breakthroughs by combining AI and mathematical optimization.

“The use-inspired research in AI4OPT addresses fundamental societal and technological challenges,” said Pascal Van Hentenryck, AI4OPT director. “The energy grid is crucial to our daily lives. Our collaboration with Los Alamos advances a research mission and educational vision with significant impact for science and society.”

The three-year agreement, funded through the Laboratory Directed Research and Development program’s ArtIMis initiative, runs through 2027. It supports the Laboratory’s commitment to advancing AI. Earl Lawrence is the project’s principal investigator, with Diane Oyen and Emily Castleton joining Bent as co-principal investigators.

Bent, Castleton, Lawrence, and Oyen are also members of the AI Council at the Laboratory. The AI Council helps the Lab navigate the evolving AI landscape, build investment capacities, and forge industry and academic partnerships.

As highlighted in the Department of Energy’s Frontiers in Artificial Intelligence for Science, Security, and Technology (FASST) initiative, AI technologies will significantly enhance the contributions of laboratories to national missions. This partnership with Georgia Tech through AI4OPT is a key step towards that future.

The Energy, Policy, and Innovation Center (EPICenter) hosted the 2024 cohort of Energy Unplugged, a Science, Technology, Engineering, Art, and Math (STEAM) summer program for high school students. The weeklong camp was held at Georgia Tech’s Atlanta and Savannah campuses this summer and has earned a reputation as one of the most sought-after high-school-level summer camps hosted by Georgia Tech. 

Rich Simmons, director of Research and Studies at the Strategic Energy Institute, has been the driving force behind the camp since its inception in 2019. Simmons, a faculty instructor in the George W. Woodruff School of Mechanical Engineering at Georgia Tech, brings his award-winning teaching expertise to high school students, ensuring that each session of Energy Unplugged is both educational and engaging. The program covered a range of timely topics, from basic energy principles such as conservation laws, electric circuits, and battery storage to more complex subjects like environmental impacts, data analytics, and decision-making. In addition, students were immersed in hands-on activities, interactive demonstrations, and power plant site visits.

During the first two days, students formed teams to construct catapults and mousetrap cars, discussed the underlying physics involving energy conversion, and then launched projectiles and vehicles to test their predictions. In one of the camp’s most popular activities, students raced remote-controlled cars around an obstacle course to learn about the importance of balancing multiple objectives, such as energy use, elapsed time, safety, and cargo capacity. The week culminated in a small-group mini-project, where campers applied the skills they had acquired to solve a real-world challenge — to optimize a cooking process using solar energy. Given specific parameters on energy generation, storage, and meal demand, the students determined the best approach to convert solar energy for cooking and storage to meet a daily lunch and dinner schedule for a food truck business. The program concluded with the campers presenting their preferred designs to an audience of parents, faculty, and staff, adding public speaking and technical presentation skills to their summer experiences.

Every year, students highlight the energy field trips to power plants, data centers, robotics labs, and makerspaces as some of their favorite aspects of the camp. A student poll during the final presentations used words like fun, informative, interesting, magical, epic, exciting, educational, and fantastical to describe the camping experience. The camp introduced the students to STEM-related careers and the many undergraduate programs that could provide a pathway for them. 

Energy Unplugged provides a portal for Georgia Tech graduate student interns such as Jake Churchill and staff members such as Jordann Shields to engage students with energy concepts, activities, career paths, and information about attending Georgia Tech. 

Energy Unplugged is administered by Georgia Tech Summer P.E.A.K.S. (Program for Enrichment and Accelerated Knowledge in STEAM) at CEISMC (the Center for Education Integrating Science, Mathematics, and Computing), the primary connection point between Tech faculty and students and the K-12 STEAM education community. Annually, CEISMC programs are accessible to more than 39,000 students; 1,700 teachers; and 200 schools in over 75 school districts throughout Georgia.

As part of the Strategic Energy Institute, EPICenter taps into regional and national expertise within academia, businesses, non-governmental organizations, and research facilities to provide an unbiased and interdisciplinary framework for driving innovation in energy policy and technology in the Southeast.

For some students, an 8 a.m. class will take away the morning jog they enjoyed every day last semester. For others, a lab meeting time changed, and tennis doubles in the afternoon won’t be an option anymore.

Students returning to campus for a new semester often struggle to find time for physical activities because of their new routines and schedules. However, a new app developed at Georgia Tech helps busy students prioritize physical activity in their daily routines.

Ph.D. student Kefan Xu of the Ubicomp Health and Wellness Lab at Georgia Tech created Plannergy, a time management app that identifies open time blocks in users’ schedules. 

Xu introduced Plannergy at the Conference on Human Factors in Computing (CHI) in Honolulu, Hawaii in May. He says the app is ideal for college students because they tend to have busy and inconsistent schedules.

Plannergy allows users to track their schedules, reflect on what activities would be beneficial and timely, and strategize how to implement the activity into their schedule.

"Currently, the app is catered to people who’ve been physically inactive and have inconsistent schedules,” Xu said. “College students know their schedule will change when they begin a new semester. They need to get some physical activity and find opportunities in the day they can leverage. It could be as simple as walking to school instead of taking a scooter.”

Xu tested his app on 16 college students who planned their physical activities every seven days and followed a reflective iteration framework to track improvement. The results showed that Plannergy is an effective behavior change tool. The findings also indicate that it increases participants’ awareness of their schedules.

The American Heart Association says adults can reduce the risk of heart disease by participating in at least 150 minutes of moderate-intensity physical activity weekly.

The Centers for Disease Control and Prevention released a report in 2023 that found 72% of Americans aren’t meeting that standard.

As Xu points out in his paper, studies have shown that incorporating physical activity into a person’s routine usually helps them maintain it. However, he’s identified two common problems:

• People lack understanding about their schedules and routines.
• People have schedules that fluctuate from one day to the next.

“Individuals face a lot of changes in their life,” Xu said. “Maybe they’re a student who has graduated, and they’re going into industry, which means their daily routine will be different from what it was while they were in school. This app allows them to experiment with different time slots and activity types to figure out another way and help them update their activity routine no matter what life changes they face.”

CUSTOM FIT

Some users who have been inactive for extended periods may be unsure how much exercise they need. Plannergy can also help them determine the intensity level of the activity to help avoid overexertion. 

“If someone has been inactive for months, it’s hard to ask them to run two miles daily,” Xu said. “There’s much for them to figure out. How much do they want to do, and at what intensity level? This app lets them gradually figure out the ideal activity. They can continue to track their progress and see if improvements are needed.”

Plannergy is not limited to physical activity. Xu says one of the students in his study who worked out daily used the app to identify times in her schedule to take breaks or focus on more spiritual disciplines.

“She added yoga and removed some high-intensity physical activities, and her sleeping routine also changed,” Xu said.

Xu is working to improve the app. Future versions will have sensing technology to leverage health informatics so users can make better decisions. He also wants the app to record user data and make customized suggestions for activities that fit the user’s schedule and preferred exercise intensity level.

“The app requires manual tracking, which can create user burden,” he said. “I think in the future, the process could be more automated. We want to keep it flexible but add more scaffolding to enhance user experience.”

 

Nylon, Teflon, Kevlar. These are just a few familiar polymers — large-molecule chemical compounds — that have changed the world. From Teflon-coated frying pans to 3D printing, polymers are vital to creating the systems that make the world function better. 

Finding the next groundbreaking polymer is always a challenge, but now Georgia Tech researchers are using artificial intelligence (AI) to shape and transform the future of the field. Rampi Ramprasad’s group develops and adapts AI algorithms to accelerate materials discovery. 

This summer, two papers published in the Nature family of journals highlight the significant advancements and success stories emerging from years of AI-driven polymer informatics research. The first, featured in Nature Reviews Materials, showcases recent breakthroughs in polymer design across critical and contemporary application domains: energy storage, filtration technologies, and recyclable plastics. The second, published in Nature Communications, focuses on the use of AI algorithms to discover a subclass of polymers for electrostatic energy storage, with the designed materials undergoing successful laboratory synthesis and testing. 

“In the early days of AI in materials science, propelled by the White House’s Materials Genome Initiative over a decade ago, research in this field was largely curiosity-driven,” said Ramprasad, a professor in the School of Materials Science and Engineering. “Only in recent years have we begun to see tangible, real-world success stories in AI-driven accelerated polymer discovery. These successes are now inspiring significant transformations in the industrial materials R&D landscape. That’s what makes this review so significant and timely.”

AI Opportunities

Ramprasad’s team has developed groundbreaking algorithms that can instantly predict polymer properties and formulations before they are physically created. The process begins by defining application-specific target property or performance criteria. Machine learning (ML) models train on existing material-property data to predict these desired outcomes. Additionally, the team can generate new polymers, whose properties are forecasted with ML models. The top candidates that meet the target property criteria are then selected for real-world validation through laboratory synthesis and testing. The results from these new experiments are integrated with the original data, further refining the predictive models in a continuous, iterative process. 

While AI can accelerate the discovery of new polymers, it also presents unique challenges. The accuracy of AI predictions depends on the availability of rich, diverse, extensive initial data sets, making quality data paramount. Additionally, designing algorithms capable of generating chemically realistic and synthesizable polymers is a complex task. 

The real challenge begins after the algorithms make their predictions: proving that the designed materials can be made in the lab and function as expected and then demonstrating their scalability beyond the lab for real-world use. Ramprasad’s group designs these materials, while their fabrication, processing, and testing are carried out by collaborators at various institutions, including Georgia Tech. Professor Ryan Lively from the School of Chemical and Biomolecular Engineering frequently collaborates with Ramprasad’s group and is a co-author of the paper published in Nature Reviews Materials.

"In our day-to-day research, we extensively use the machine learning models Rampi’s team has developed,” Lively said. “These tools accelerate our work and allow us to rapidly explore new ideas. This embodies the promise of ML and AI because we can make model-guided decisions before we commit time and resources to explore the concepts in the laboratory."

Using AI, Ramprasad’s team and their collaborators have made significant advancements in diverse fields, including energy storage, filtration technologies, additive manufacturing, and recyclable materials.

Polymer Progress

One notable success, described in the Nature Communications paper, involves the design of new polymers for capacitors, which store electrostatic energy. These devices are vital components in electric and hybrid vehicles, among other applications. Ramprasad’s group worked with researchers from the University of Connecticut.

Current capacitor polymers offer either high energy density or thermal stability, but not both. By leveraging AI tools, the researchers determined that insulating materials made from polynorbornene and polyimide polymers can simultaneously achieve high energy density and high thermal stability. The polymers can be further enhanced to function in demanding environments, such as aerospace applications, while maintaining environmental sustainability. 

“The new class of polymers with high energy density and high thermal stability is one of the most concrete examples of how AI can guide materials discovery,” said Ramprasad. “It is also the result of years of multidisciplinary collaborative work with Greg Sotzing and Yang Cao at the University of Connecticut and sustained sponsorship by the Office of Naval Research.”

Industry Potential

The potential for real-world translation of AI-assisted materials development is underscored by industry participation in the Nature Reviews Materials article. Co-authors of this paper also include scientists from Toyota Research Institute and General Electric. To further accelerate the adoption of AI-driven materials development in industry, Ramprasad co-founded Matmerize Inc., a software startup company recently spun out of Georgia Tech. Their cloud-based polymer informatics software is already being used by companies across various sectors, including energy, electronics, consumer products, chemical processing, and sustainable materials. 

“Matmerize has transformed our research into a robust, versatile, and industry-ready solution, enabling users to design materials virtually with enhanced efficiency and reduced cost,” Ramprasad said. “What began as a curiosity has gained significant momentum, and we are entering an exciting new era of materials by design.”

 

 

While the U.S. federal government has clean energy targets, they are not binding. Most economically developed countries have mandatory policies designed to bolster renewable electricity production. Because the U.S. lacks an enforceable federal mandate for renewable electricity, individual states are left to develop their own regulations. 

Marilyn Brown, Regents’ and Brook Byers Professor of Sustainable Systems in Georgia Tech’s School of Public Policy; Shan Zhou, an assistant professor at Purdue University and Georgia Tech Ph.D. alumna; and Barry Solomon, a professor emeritus of environmental policy at Michigan Technological University, investigated how clean electricity policies affect not only the states that adopt them, but neighboring states as well. Using data-driven comparisons, the researchers found that the impact of these subnational clean energy policies is far greater — and more nuanced — than previously known. 

Their research was recently published in the journal Proceedings of the National Academy of Sciences. 

“Analysts are asking if the U.S. should have a federal renewable mandate to put the whole country on the same page, or if individual state policies are sufficient,” Brown said. “To answer that question, it is useful to know if states with renewable energy policies are influencing those without them.”

Brown, Solomon, and Zhou examined a common clean energy policy tool: the Renewable Portfolio Standard (RPS). Adopted by more than half of U.S. states, RPSs are regulations requiring a state’s utility providers to generate a certain percentage of their electricity from renewable resources, such as wind or solar. Many of these standards are mandatory, with utility companies facing fines if they fail to reach targets within a given time.

To investigate the influence of these policies across state lines, the researchers first created a dataset that included 31 years (1991-2021) of annual renewable electricity generation data for 48 U.S. states and the District of Columbia. They then used the dataset to generate pairs of states linking each state to its geographic neighbors or electricity trading partners, allowing them to examine the influence of the RPS policy adopted by one of the pair on the renewable energy generation of the other — a total of 1,519 paired comparisons. 

“By only looking at the pairs, we can see if an RPS in one state directly affects renewable electricity generation in another state, and, if that’s the case, whether it is because they are geographic neighbors or if it’s because they are participating in the same wholesale electricity market,” Zhou said. 

Looking into the electricity market is important, because states often purchase electricity from other states through wholesale markets rather than exclusively producing their own power, and the purchased power can be generated from renewables. Utilities in some states may be allowed to meet their own RPS requirements by purchasing renewable energy credits based on the renewable electricity generated in other states. 

In their analyses, the team also considered the concept of “policy stringency.” A stringency measure evaluates a state’s renewable electricity targets relative to the amount currently produced in the state. For example, if a state requires electric utilities to generate 30% of their electricity from renewable sources by 2030 and the state already has 25%, it isn’t a very stringent policy. On the other hand, if a state has a 30% target and only uses 10% renewables currently, it has a more ambitious and stringent RPS.

Though policy experts have used the metric in related work for over a decade, the research team improved the design. 

“Our stringency variable includes interim targets as well as the existing share of renewable energy generation,” Solomon said.

The team found that the amount of renewable electricity generation in a state is not only influenced by whether that state has its own RPS, but also by the RPS policies of neighboring states. 

“We also learned that the stronger a neighboring state’s RPS policy is, the more likely a given state is to generate more renewable electricity,” Brown said. “It’s all a very interactive web with many co-benefits.”

The authors were surprised to find that a given state’s electricity trading partners did not hold the most influence over renewable generation, but rather the geographical proximity to RPS states. They suggest that past RPS policy research focusing on within-state impacts likely underestimated an RPS’s full impact. While the researchers have not yet identified all factors that can cause spillover effects, they plan to investigate this further. 

“The spillover effect is very significant and should not be overlooked by future research, especially for states without RPSs,” Zhou said. “For states without policies, their renewable electricity generation is very heavily influenced by their neighbors.”

Citation: Shan Zhou, Barry D. Solomon, and Marilyn A. Brown, “The spillover effect of mandatory renewable portfolio standards.” PNAS (June 2024). 
DOI: https://doi.org/10.1073/pnas.2313193121
 

 

Anu Iyer, a Georgia Tech Dean’s Scholar, published her first research article as a first-year student — based on research conducted while she was in high school. She is the lead co-author of the paper published in Scientific Reports, a Nature Portfolio journal.

Iyer, now a second-year undergraduate majoring in biology with a pre-med focus, worked with researchers at the University of Arkansas for Medical Sciences (UAMS) to develop a novel voice-based diagnostic tool for Parkinson’s disease (PD).

“Essentially, we proved the feasibility of a telemedicine approach towards detecting PD,” says Iyer. “Through a three-second phone call, our machine-learning model recognizes patterns in data to detect Parkinson’s with a 97 percent accuracy rate.”

Iyer states that additional strengths of the project include the potential for detecting PD at an early stage, leading to improved treatment outcomes, and the practical benefits of a virtual diagnostic tool.

“Parkinson’s disease is a nervous system disorder that primarily affects the elderly population, and one of the many issues with detection is that symptoms must be analyzed in person,” explains Iyer. “In Arkansas, 75 percent of our population resides in medically underserved areas — it can be hard for them to access health facilities. Our research addresses the need for convenient detection via telemedicine.”

From science fairs to academic researcher

Iyer’s teachers at her STEM middle school encouraged her passion for science and discovery. A science fair enthusiast, Iyer led a sixth-grade team to win the state title for the Verizon Innovative Learning app, creating a smartphone app that turns off text notifications when a car reaches more than five miles per hour.

Iyer credits her middle school teachers for inspiring her to seek answers beyond what she found in her textbooks. During the summer between eighth and ninth grade, Iyer watched YouTube videos to teach herself machine learning, appreciating the opportunity to use artificial intelligence to analyze data and make predictions.

“Machine learning fascinates me because it holds so much potential,” says Iyer. “I've always been interested in computer science, but machine learning opened my eyes to new possibilities and taught me that I can pay it forward through applied bioinformatics.”

In ninth grade, she emailed UAMS professors with a research idea incorporating medicine and computer science. Her outreach led to a post as an undergraduate researcher, helping create a computer algorithm to detect eye disease. While working on a diagnostic AI model for malignancy, she began collaborating with Fred Prior, the chair of Bioinformatics at UAMS, who became a valued mentor.

“Dr. Prior introduced me to the joys of research and how small changes can make a big difference in our world,” says Iyer.

Prior assigned her to the team focusing on Parkinson’s in her 11th grade year — and she soon began taking on more of an active leadership role in the research. She spent the rest of high school juggling coursework with constructing code and drafting proposals to create the computer algorithm capable of detecting PD.

Progress and service

Iyer’s desire to improve the world through research led her to Georgia Tech.

“One thing that spoke to me is the Progress and Service motto,” says Iyer. “My career goals include becoming an empathetic researcher focused on reducing healthcare disparities. Specifically, I hope to specialize in developing diagnostic tools that are affordable and available for underserved areas.”

As lead co-author of the PD research study, Iyer spent much of her first year working with Prior and UAMS, participating in Zoom calls every Saturday. As a second-year, Iyer intends to continue working with UAMS on PD and machine-learning research. She has also taken on a new role as multiple principal investigator for a study related to chronic back pain management.

Lainie Pomerleau, who taught Iyer’s first-year English course, and is now an assistant professor of English at the College of Coastal Georgia, helped Iyer prepare the PD paper for publication. “Anu embodies Georgia Tech's mission to develop leaders who advance technology to improve the human condition,” says Pomerleau.

Despite her busy schedule, Iyer has immersed herself in the Georgia Tech community. She loves the climbing wall at the Campus Recreation Center and points to Cognitive Psychology as her favorite class. Iyer considers Explore, the science-centered living and learning community, to be one of the highlights of her first year.

“I really enjoyed being a part of Explore, living with other students who prioritize science,” says Iyer. “It was easy to make friends because we all had similar classes.”

In the spring of her first year, she was selected as a College of Sciences Ambassador, accompanying prospective students and their parents to science-related courses and answering their questions about campus life.

She plans to get more involved with researchers at Georgia Tech.

“I am a biology major, but one amazing thing about Georgia Tech is that there is a lot of encouragement to join labs outside of your major and pursue your interests,” says Iyer. “I’d like to work in a Georgia Tech lab, particularly in neurology.”

Looking forward to her next few years at the Institute, she’s excited about the possibilities ahead:

“Georgia Tech is well known for groundbreaking research,” she says. “I want to take advantage of Tech’s many opportunities — and fulfill my ultimate goal of making a positive impact in the world.”

From data breaches to widespread systemic shutdowns, cyberattacks like the  2024 Fulton County (Georgia) government attack now occur as regularly as natural disasters — and cause just as much destruction. And, like severe weather, they can be predicted thanks to a new artificial intelligence (AI) tool that analyzes social media to determine who could cause the next big cyberattack. 

Researchers in Georgia Tech’s Scheller College of Business, together with colleagues at the University of the District of Columbia, Washington, D.C. (UDC),  developed a chatbot that analyzed sentiment on popular social media sites like X (formerly known as Twitter) to determine cyberthreats. The chatbot tweeted information to engage Twitter users who either tweeted about news events or holidays, or retweeted cyberattack news. It interacted with 100,000 users over a three-month period. Sentiment analysis — gauging users’ feelings, attitudes, and moods— was performed on human responses to the bot’s tweets.  

Applying sentiment analysis to human-chatbot interactions is not new. Globally, companies use chatbots to determine customers’ reactions to brands and products.  During the Covid-19 pandemic, governments and health organizations employed chatbots to determine public attitudes toward vaccinations, preventive measures, and mask wearing. However, identifying potential cyberthreats via sentiment analysis represents a unique — and complicated — application.

“When you examine sentiment analysis on a chatbot through a cybersecurity lens, you are looking for potential hackers,” said Scheller Professor John McIntyre, who is also the executive director of the Center for International Business Education and Research.  “Catching hackers using sentiment analysis is challenging, but  predictive models can be built to find them. 

“AI can target a particular population to understand its expressions of approval, disapproval, or even intent to harm, attack, or misuse the technology.”

A team led by McIntyre and UDC Associate Professors Amit Arora and Anshu Arora conducted the research. They set out to see if cybersecurity threats could be discovered through social media, but the study is just the beginning of a potentially fertile cyberthreat prevention method. McIntyre believes the study could expand to analyzing sentiment in other languages and even on other platforms. 

“As we move toward a world in which we'll rely more and more on communication technologies and social media, there will be an increasing number of threats,” he said. “We must know how to counter such threats.”

Funded by the Department of Defense’s Applied Research Lab for Intelligence and Security

Arora A, Arora A, McIntyre J. Developing Chatbots for Cyber Security: Assessing Threats through Sentiment Analysis on Social Media. Sustainability. 2023; 15(17):13178. https://doi.org/10.3390/su151713178