Origami — the Japanese art of folding paper — could be at the next frontier in innovative materials.

Practiced in Japan since the early 1600s, origami involves combining simple folding techniques to create intricate designs. Now, Georgia Tech researchers are leveraging the technique as the foundation for next-generation materials that can both act as a solid and predictably deform, “folding” under the right forces. The research could lead to innovations in everything from heart stents to airplane wings and running shoes.

Recently published in Nature Communications, the study, “Coarse-grained fundamental forms for characterizing isometries of trapezoid-based origami metamaterials,” was led by first author James McInerney, who is now a NRC Research Associate at the Air Force Research Laboratory. McInerney, who completed the research while a postdoctoral student at the University of Michigan, was previously a doctoral student at Georgia Tech in the group of study co-author Zeb Rocklin. The team also includes Glaucio Paulino (Princeton University), Xiaoming Mao (University of Michigan), and Diego Misseroni (University of Trento).

“Origami has received a lot of attention over the past decade due to its ability to deploy or transform structures,” McInerney says. “Our team wondered how different types of folds could be used to control how a material deforms when different forces and pressures are applied to it” — like a creased piece of cardboard folding more predictably than one that might crumple without any creases.

The applications of that type of control are vast. “There are a variety of scenarios ranging from the design of buildings, aircraft, and naval vessels to the packaging and shipping of goods where there tends to be a trade-off between enhancing the load-bearing capabilities and increasing the total weight,” McInerney explains. “Our end goal is to enhance load-bearing designs by adding origami-inspired creases — without adding weight.”

The challenge, Rocklin adds, is using physics to find a way to predictably model what creases to use and when to achieve the best results.

Deformable solids

Rocklin, a theoretical physicist and associate professor in the School of Physics at Georgia Tech, emphasizes the complex nature of these types of materials. “If I tug on either end of a sheet of paper, it's solid — it doesn’t separate,” he explains. “But it's also flexible — it can crumple and wave depending on how I move it. That’s a very different behavior than what we might see in a conventional solid, and a very useful one.”

But while flexible solids are uniquely useful, they are also very hard to characterize, he says. “With these materials, it is often difficult to predict what is going to happen — how the material will deform under pressure because they can deform in many different ways. Conventional physics techniques can't solve this type of problem, which is why we're still coming up with new ways to characterize structures in the 21st century.”

When considering origami-inspired materials, physicists start with a flat sheet that's carefully creased to create a specific three-dimensional shape; these folds determine how the material behaves. But the method is limited: only parallelogram-based origami folding, which uses shapes like squares and rectangles, had previously been modeled, allowing for limited types of deformation.

“Our goal was to expand on this research to include trapezoid faces,” McInerney says. Parallelograms have two sets of parallel sides, but trapezoids only need to have one set of parallel sides. Introducing these more variable shapes makes this type of creasing more difficult to model, but potentially more versatile.

Breathing and shearing

“From our models and physical tests, we found that trapezoid faces have an entirely different class of responses,” McInerney shares. In other words — using trapezoids leads to new behavior.

The designs had the ability to change their shape in two distinct ways: "breathing" by expanding and contracting evenly, and “shearing" by deforming in a twisting motion. “We learned that we can use trapezoid faces in origami to constrain the system from bending in certain directions, which provides different functionality than parallelogram faces,” McInerney adds. 

Surprisingly, the team also found that some of the behavior in parallelogram-based origami carried over to their trapezoidal origami, hinting at some features that might be universal across designs.

“While our research is theoretical, these insights could give us more opportunities for how we might deploy these structures and use them,” Rocklin shares.

Future folding

“We still have a lot of work to do,” McInerney says, sharing that there are two separate avenues of research to pursue. “The first is moving from trapezoids to more general quadrilateral faces, and trying to develop an effective model of the material behavior — similar to the way this study moved from parallelograms to trapezoids.” Those new models could help predict how creased materials might deform under different circumstances, and help researchers compare those results to sheets without any creases at all. “This will essentially let us assess the improvement our designs provide,” he explains.

“The second avenue is to start thinking deeply about how our designs might integrate into a real system,” McInerney continues. “That requires understanding where our models start to break down, whether it is due to the loading conditions or the fabrication process, as well as establishing effective manufacturing and testing protocols.”

“It’s a very challenging problem, but biology and nature are full of smart solids — including our own bodies — that deform in specific, useful ways when needed,” Rocklin says. “That’s what we’re trying to replicate with origami.”

This research was funded by the Office of Naval Research, European Union, Army Research Office, and National Science Foundation.

DOI: https://doi.org/10.1038/s41467-025-57089-x 

Many communities rely on insights from computer-based models and simulations. This week, a nest of Georgia Tech experts are swarming an international conference to present their latest advancements in these tools, which offer solutions to pressing challenges in science and engineering.

Students and faculty from the School of Computational Science and Engineering (CSE) are leading the Georgia Tech contingent at the SIAM Conference on Computational Science and Engineering (CSE25). The Society of Industrial and Applied Mathematics (SIAM) organizes CSE25, occurring March 3-7 in Fort Worth, Texas.

At CSE25, the School of CSE researchers are presenting papers that apply computing approaches to varying fields, including:                   

• Experiment designs to accelerate the discovery of material properties
• Machine learning approaches to model and predict weather forecasting and coastal flooding
• Virtual models that replicate subsurface geological formations used to store captured carbon dioxide
• Optimizing systems for imaging and optical chemistry
• Plasma physics during nuclear fusion reactions

[Related: GT CSE at SIAM CSE25 Interactive Graphic] 

“In CSE, researchers from different disciplines work together to develop new computational methods that we could not have developed alone,” said School of CSE Professor Edmond Chow. 

“These methods enable new science and engineering to be performed using computation.” 

CSE is a discipline dedicated to advancing computational techniques to study and analyze scientific and engineering systems. CSE complements theory and experimentation as modes of scientific discovery. 

Held every other year, CSE25 is the primary conference for the SIAM Activity Group on Computational Science and Engineering (SIAG CSE). School of CSE faculty serve in key roles in leading the group and preparing for the conference.

In December, SIAG CSE members elected Chow to a two-year term as the group’s vice chair. This election comes after Chow completed a term as the SIAG CSE program director. 

School of CSE Associate Professor Elizabeth Cherry has co-chaired the CSE25 organizing committee since the last conference in 2023. Later that year, SIAM members reelected Cherry to a second, three-year term as a council member at large. 

At Georgia Tech, Chow serves as the associate chair of the School of CSE. Cherry, who recently became the associate dean for graduate education of the College of Computing, continues as the director of CSE programs. 

“With our strong emphasis on developing and applying computational tools and techniques to solve real-world problems, researchers in the School of CSE are well positioned to serve as leaders in computational science and engineering both within Georgia Tech and in the broader professional community,” Cherry said. 

Georgia Tech’s School of CSE was first organized as a division in 2005, becoming one of the world’s first academic departments devoted to the discipline. The division reorganized as a school in 2010 after establishing the flagship CSE Ph.D. and M.S. programs, hiring nine faculty members, and attaining substantial research funding.

Ten School of CSE faculty members are presenting research at CSE25, representing one-third of the School’s faculty body. Of the 23 accepted papers written by Georgia Tech researchers, 15 originate from School of CSE authors.

The list of School of CSE researchers, paper titles, and abstracts includes:
Bayesian Optimal Design Accelerates Discovery of Material Properties from Bubble Dynamics
Postdoctoral Fellow Tianyi Chu, Joseph Beckett, Bachir Abeid, and Jonathan Estrada (University of Michigan), Assistant Professor Spencer Bryngelson
[Abstract]

Latent-EnSF: A Latent Ensemble Score Filter for High-Dimensional Data Assimilation with Sparse Observation Data
Ph.D. student Phillip Si, Assistant Professor Peng Chen
[Abstract]

A Goal-Oriented Quadratic Latent Dynamic Network Surrogate Model for Parameterized Systems
Yuhang Li, Stefan Henneking, Omar Ghattas (University of Texas at Austin), Assistant Professor Peng Chen
[Abstract]

Posterior Covariance Structures in Gaussian Processes
Yuanzhe Xi (Emory University), Difeng Cai (Southern Methodist University), Professor Edmond Chow
[Abstract]

Robust Digital Twin for Geological Carbon Storage
Professor Felix Herrmann, Ph.D. student Abhinav Gahlot, alumnus Rafael Orozco (Ph.D. CSE-CSE 2024), alumnus Ziyi (Francis) Yin (Ph.D. CSE-CSE 2024), and Ph.D. candidate Grant Bruer
[Abstract]

Industry-Scale Uncertainty-Aware Full Waveform Inference with Generative Models
Rafael Orozco, Ph.D. student Tuna Erdinc, alumnus Mathias Louboutin (Ph.D. CS-CSE 2020), and Professor Felix Herrmann
[Abstract]

Optimizing Coupled Systems: Insights from Co-Design Imaging and Optical Chemistry
Assistant Professor Raphaël Pestourie, Wenchao Ma and Steven Johnson (MIT), Lu Lu (Yale University), Zin Lin (Virginia Tech)
[Abstract]

Multifidelity Linear Regression for Scientific Machine Learning from Scarce Data
Assistant Professor Elizabeth Qian, Ph.D. student Dayoung Kang, Vignesh Sella, Anirban Chaudhuri and Anirban Chaudhuri (University of Texas at Austin)
[Abstract]

LyapInf: Data-Driven Estimation of Stability Guarantees for Nonlinear Dynamical Systems
Ph.D. candidate Tomoki Koike and Assistant Professor Elizabeth Qian
[Abstract]

The Information Geometric Regularization of the Euler Equation
Alumnus Ruijia Cao (B.S. CS 2024), Assistant Professor Florian Schäfer
[Abstract]

Maximum Likelihood Discretization of the Transport Equation
Ph.D. student Brook Eyob, Assistant Professor Florian Schäfer
[Abstract]

Intelligent Attractors for Singularly Perturbed Dynamical Systems
Daniel A. Serino (Los Alamos National Laboratory), Allen Alvarez Loya (University of Colorado Boulder), Joshua W. Burby, Ioannis G. Kevrekidis (Johns Hopkins University), Assistant Professor Qi Tang (Session Co-Organizer)
[Abstract]

Accurate Discretizations and Efficient AMG Solvers for Extremely Anisotropic Diffusion Via Hyperbolic Operators
Golo Wimmer, Ben Southworth, Xianzhu Tang (LANL), Assistant Professor Qi Tang 
[Abstract]

Randomized Linear Algebra for Problems in Graph Analytics
Professor Rich Vuduc
[Abstract]

Improving Spgemm Performance Through Reordering and Cluster-Wise Computation
Assistant Professor Helen Xu
[Abstract]

Many communities rely on insights from computer-based models and simulations. This week, a nest of Georgia Tech experts are swarming an international conference to present their latest advancements in these tools, which offer solutions to pressing challenges in science and engineering.

Students and faculty from the School of Computational Science and Engineering (CSE) are leading the Georgia Tech contingent at the SIAM Conference on Computational Science and Engineering (CSE25). The Society of Industrial and Applied Mathematics (SIAM) organizes CSE25, occurring March 3-7 in Fort Worth, Texas.

At CSE25, the School of CSE researchers are presenting papers that apply computing approaches to varying fields, including:                   

• Experiment designs to accelerate the discovery of material properties
• Machine learning approaches to model and predict weather forecasting and coastal flooding
• Virtual models that replicate subsurface geological formations used to store captured carbon dioxide
• Optimizing systems for imaging and optical chemistry
• Plasma physics during nuclear fusion reactions

[Related: GT CSE at SIAM CSE25 Interactive Graphic] 

“In CSE, researchers from different disciplines work together to develop new computational methods that we could not have developed alone,” said School of CSE Professor Edmond Chow. 

“These methods enable new science and engineering to be performed using computation.” 

CSE is a discipline dedicated to advancing computational techniques to study and analyze scientific and engineering systems. CSE complements theory and experimentation as modes of scientific discovery. 

Held every other year, CSE25 is the primary conference for the SIAM Activity Group on Computational Science and Engineering (SIAG CSE). School of CSE faculty serve in key roles in leading the group and preparing for the conference.

In December, SIAG CSE members elected Chow to a two-year term as the group’s vice chair. This election comes after Chow completed a term as the SIAG CSE program director. 

School of CSE Associate Professor Elizabeth Cherry has co-chaired the CSE25 organizing committee since the last conference in 2023. Later that year, SIAM members reelected Cherry to a second, three-year term as a council member at large. 

At Georgia Tech, Chow serves as the associate chair of the School of CSE. Cherry, who recently became the associate dean for graduate education of the College of Computing, continues as the director of CSE programs. 

“With our strong emphasis on developing and applying computational tools and techniques to solve real-world problems, researchers in the School of CSE are well positioned to serve as leaders in computational science and engineering both within Georgia Tech and in the broader professional community,” Cherry said. 

Georgia Tech’s School of CSE was first organized as a division in 2005, becoming one of the world’s first academic departments devoted to the discipline. The division reorganized as a school in 2010 after establishing the flagship CSE Ph.D. and M.S. programs, hiring nine faculty members, and attaining substantial research funding.

Ten School of CSE faculty members are presenting research at CSE25, representing one-third of the School’s faculty body. Of the 23 accepted papers written by Georgia Tech researchers, 15 originate from School of CSE authors.

The list of School of CSE researchers, paper titles, and abstracts includes:
Bayesian Optimal Design Accelerates Discovery of Material Properties from Bubble Dynamics
Postdoctoral Fellow Tianyi Chu, Joseph Beckett, Bachir Abeid, and Jonathan Estrada (University of Michigan), Assistant Professor Spencer Bryngelson
[Abstract]

Latent-EnSF: A Latent Ensemble Score Filter for High-Dimensional Data Assimilation with Sparse Observation Data
Ph.D. student Phillip Si, Assistant Professor Peng Chen
[Abstract]

A Goal-Oriented Quadratic Latent Dynamic Network Surrogate Model for Parameterized Systems
Yuhang Li, Stefan Henneking, Omar Ghattas (University of Texas at Austin), Assistant Professor Peng Chen
[Abstract]

Posterior Covariance Structures in Gaussian Processes
Yuanzhe Xi (Emory University), Difeng Cai (Southern Methodist University), Professor Edmond Chow
[Abstract]

Robust Digital Twin for Geological Carbon Storage
Professor Felix Herrmann, Ph.D. student Abhinav Gahlot, alumnus Rafael Orozco (Ph.D. CSE-CSE 2024), alumnus Ziyi (Francis) Yin (Ph.D. CSE-CSE 2024), and Ph.D. candidate Grant Bruer
[Abstract]

Industry-Scale Uncertainty-Aware Full Waveform Inference with Generative Models
Rafael Orozco, Ph.D. student Tuna Erdinc, alumnus Mathias Louboutin (Ph.D. CS-CSE 2020), and Professor Felix Herrmann
[Abstract]

Optimizing Coupled Systems: Insights from Co-Design Imaging and Optical Chemistry
Assistant Professor Raphaël Pestourie, Wenchao Ma and Steven Johnson (MIT), Lu Lu (Yale University), Zin Lin (Virginia Tech)
[Abstract]

Multifidelity Linear Regression for Scientific Machine Learning from Scarce Data
Assistant Professor Elizabeth Qian, Ph.D. student Dayoung Kang, Vignesh Sella, Anirban Chaudhuri and Anirban Chaudhuri (University of Texas at Austin)
[Abstract]

LyapInf: Data-Driven Estimation of Stability Guarantees for Nonlinear Dynamical Systems
Ph.D. candidate Tomoki Koike and Assistant Professor Elizabeth Qian
[Abstract]

The Information Geometric Regularization of the Euler Equation
Alumnus Ruijia Cao (B.S. CS 2024), Assistant Professor Florian Schäfer
[Abstract]

Maximum Likelihood Discretization of the Transport Equation
Ph.D. student Brook Eyob, Assistant Professor Florian Schäfer
[Abstract]

Intelligent Attractors for Singularly Perturbed Dynamical Systems
Daniel A. Serino (Los Alamos National Laboratory), Allen Alvarez Loya (University of Colorado Boulder), Joshua W. Burby, Ioannis G. Kevrekidis (Johns Hopkins University), Assistant Professor Qi Tang (Session Co-Organizer)
[Abstract]

Accurate Discretizations and Efficient AMG Solvers for Extremely Anisotropic Diffusion Via Hyperbolic Operators
Golo Wimmer, Ben Southworth, Xianzhu Tang (LANL), Assistant Professor Qi Tang 
[Abstract]

Randomized Linear Algebra for Problems in Graph Analytics
Professor Rich Vuduc
[Abstract]

Improving Spgemm Performance Through Reordering and Cluster-Wise Computation
Assistant Professor Helen Xu
[Abstract]

The Institute for Matter and Systems (IMS) has received $700,000 in funding from the National Science Foundation (NSF) for two education and outreach programs. 

The awards will support the Research Experience for Undergraduates (REU) and Research Experience for Teachers (RET) programs at Georgia Tech. The REU summer internship program provides undergraduate students from two- and four-year programs the chance to perform cutting-edge research at the forefront of nanoscale science and engineering. The RET program for high school teachers and technical college faculty offers a paid opportunity to experience the excitement of nanotechnology research and to share this experience in their classrooms. 

“This NSF funding allows us to be able to do more with the programs,” said Mikkel Thomas, associate director for education and outreach. “These are programs that have existed in the past, but we haven’t had external funding for the last three years. The NSF support allows us to do more —  bring more students into the program or increase the RET stipends.”

In addition to the REU and RET programs, IMS offers short courses and workshops focused on professional development, instructional labs for undergraduate and graduate students, a certificate for veterans in microelectronics and nano-manufacturing, and community engagement activities such as the Atlanta Science Festival. 

The Cloud Hub, a key initiative of the Institute for Data Engineering and Science (IDEaS) at Georgia Tech, recently concluded a successful Call for Proposals focused on advancing the field of Generative Artificial Intelligence (GenAI). This initiative, made possible by a generous gift funding from Microsoft, aims to push the boundaries of GenAI research by supporting projects that explore both foundational aspects and innovative applications of this cutting-edge technology.

Call for Proposals: A Gateway to Innovation

Launched in early 2024, the Call for Proposals invited researchers from across Georgia Tech to submit their innovative ideas on GenAI. The scope was broad, encouraging proposals that spanned foundational research, system advancements, and novel applications in various disciplines, including arts, sciences, business, and engineering. A special emphasis was placed on projects that addressed responsible and ethical AI use.

The response from the Georgia Tech research community was overwhelming, with 76 proposals submitted by teams eager to explore this transformative technology. After a rigorous selection process, eight projects were selected for support. Each awarded team will also benefit from access to Microsoft’s Azure cloud resources..

Recognizing Microsoft’s Generous Contribution

This successful initiative was made possible through the generous support of Microsoft, whose contribution of research resources has empowered Georgia Tech researchers to explore new frontiers in GenAI. By providing access to Azure’s advanced tools and services, Microsoft has played a pivotal role in accelerating GenAI research at Georgia Tech, enabling researchers to tackle some of the most pressing challenges and opportunities in this rapidly evolving field.

Looking Ahead: Pioneering the Future of GenAI

The awarded projects, set to commence in Fall 2024, represent a diverse array of research directions, from improving the capabilities of large language models to innovative applications in data management and interdisciplinary collaborations. These projects are expected to make significant contributions to the body of knowledge in GenAI and are poised to have a lasting impact on the industry and beyond.

IDEaS and the Cloud Hub are committed to supporting these teams as they embark on their research journeys. The outcomes of these projects will be shared through publications and highlighted on the Cloud Hub web portal, ensuring visibility for the groundbreaking work enabled by this initiative.

Congratulations to the Fall 2024 Winners

• Annalisa Bracco | EAS "Modeling the Dispersal and Connectivity of Marine Larvae with GenAI Agents" [proposal co-funded with support from the Brook Byers Institute for Sustainable Systems]
• Yunan Luo | CSE “Designing New and Diverse Proteins with Generative AI”
• Kartik Goyal | IC “Generative AI for Greco-Roman Architectural Reconstruction: From Partial Unstructured Archaeological Descriptions to Structured Architectural Plans”
• Victor Fung | CSE “Intelligent LLM Agents for Materials Design and Automated Experimentation”
• Noura Howell | LMC “Applying Generative AI for STEM Education: Supporting AI literacy and community engagement with marginalized youth”
• Neha Kumar | IC “Towards Responsible Integration of Generative AI in Creative Game Development”
• Maureen Linden | Design “Best Practices in Generative AI Used in the Creation of Accessible Alternative Formats for People with Disabilities”
• Surya Kalidindi | ME & MSE “Accelerating Materials Development Through Generative AI Based Dimensionality Expansion Techniques”
• Tuo Zhao | ISyE “Adaptive and Robust Alignment of LLMs with Complex Rewards”

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.”

From airplanes to soda cans, aluminum is a crucial — not to mention, an incredibly sustainable — material in manufacturing. Since 2019, Georgia Tech has partnered with Novelis, a global leader in aluminum rolling and recycling, through the Novelis Innovation Hub to advance research and business opportunities in aluminum manufacturing.

Novelis and the Georgia Institute of Technology recently co-hosted the 19th International Conference on Aluminum Alloys (ICAA19). Held on Georgia Tech's campus, this event brought together the brightest minds in aluminum technology for four days of intensive learning and networking.

Since its inception in 1986, ICAA has been the premier global forum for aluminum manufacturing innovations. This year, the conference attracted over 300 participants from 19 countries, including representatives from academia, research organizations, and industry leaders.

“The diverse mix of attendees created a rich tapestry of knowledge and experience, fostering a robust exchange of ideas,” said Naresh Thadhani, conference co-chair and professor in the School of Materials Science and Engineering

ICAA19 featured 12 symposia topics and over 250 technical presentations, delving into critical themes such as sustainability, future mobility, and next-generation manufacturing. Keynote addresses from leaders at the Aluminum Association, Airbus, and Coca-Cola set the stage for insightful discussions. Novelis Chief Technology Officer Philippe Meyer and Georgia Tech Executive Vice President for Research Chaouki Abdallah headlined the event, underscoring the importance of Novelis’ partnership with Georgia Tech.

Marking the fifth anniversary of the Novelis Innovation Hub at Georgia Tech, Hub Executive Director Shreyes Melkote says that “ICAA19 represents a prime example of the close collaboration between Novelis and the Institute, enabled by the Novelis Innovation Hub.” Melkote, a professor in the George W. Woodruff School of Mechanical Engineering, also serves as the associate director of the Georgia Tech Manufacturing Institute.

“This unique center for research, development, and technology has been instrumental in advancing aluminum innovations, exemplifying the power of partnerships in driving industry progress,” says Meyer. “As we reflect on the success of ICAA19, we remain committed to strengthening our existing partnerships and forging new alliances to accelerate innovation. The collaborative spirit showcased at the conference is a testament to our dedication to leading the aluminum industry into a more sustainable future.”

Semiconductors make our world run, but the industry faces a turning point. For decades, computer chip efficiency has doubled every two years, but that progress is slowing. To complicate the problem further, global demand for semiconductors threatens to outpace the supply. The U.S. has the opportunity to meet the growing need for chips — both by increasing domestic manufacturing and building up the workforce, which is at its lowest in decades. To bolster semiconductor research and manufacturing, in 2022, Congress passed the $52.7 billion bipartisan CHIPS and Science Act that President Joe Biden signed into law. New paradigms and pioneers are needed to make these critical advances.

Georgia Tech is playing a significant role in creating the next generation of chips, as the Institute is especially well positioned to innovate in the semiconductor field. All areas of the semiconductor stack — the components that build a chip, from hardware to artificial intelligence — are studied at Tech, and collaboration among faculty is a hallmark of its research enterprise. Such cooperation is necessary to build better chips, since they need to be reinvented in every layer of the stack.

Read the full story on GT Research News.

Georgia Tech has been selected as one of six universities globally to receive funding for the newly established Global Industrial Technology Cooperation Center. The announcement was made by the Ministry of Trade, Industry, and Energy in South Korea during the Global Open Innovation Strategy Meeting in April.

The KIAT-Georgia Tech Semiconductor Electronics Center will receive $1.8 million to establish a sustainable semiconductor electronics research partnership between Korean companies, researchers, and Georgia Tech. 

“I am thrilled to announce that we have secured funding to launch a groundbreaking collaboration between Georgia Tech’s world-class researchers and Korean companies,” said Hong Yeo, associate professor and Woodruff Faculty Fellow in the George W. Woodruff School of Mechanical Engineering and the Wallace H. Coulter Department of Biomedical Engineering. “This initiative will drive the development of cutting-edge technologies to advance semiconductor, sensors, and electronics research.”

Yeo will lead the center, and Michael Filler, interim executive director for the Institute of Electronics and Nanotechnology, and Muhannad Bakir, director of the 3D Advanced Packaging Research Center, will serve as co-PIs.

The center will focus on advancing semiconductor research, a critical area of technology that forms the backbone of modern electronics.

The Cooperation Center is a global technology collaboration platform designed to facilitate international joint research and development planning, partner matching, and local support for domestic researchers. The selection of Georgia Tech underscores the Institute’s leadership and expertise in the field of semiconductors.

Quantum sensors detect the smallest of environmental changes — for example, an atom reacting to a magnetic field. As these sensors “read” the unique behaviors of subatomic particles, they also dramatically improve scientists’ ability to measure and detect changes in our wider environment.

Monitoring these tiny changes results in a wide range of applications — from improving navigation and natural disaster forecasting, to smarter medical imaging and detection of biomarkers of disease, gravitational wave detection, and even better quantum communication for secure data sharing.

Georgia Tech physicists are pioneering new quantum sensing platforms to aid in these efforts. The research team’s latest study, “Sensing Spin Wave Excitations by Spin Defects in Few-Layer Thick Hexagonal Boron Nitride” was published in Science Advances this week. 

The research team includes School of Physics Assistant Professors Chunhui (Rita) Du and Hailong Wang (corresponding authors) alongside fellow Georgia Tech researchers Jingcheng Zhou, Mengqi Huang, Faris Al-matouq, Jiu Chang, Dziga Djugba, and Professor Zhigang Jiang and their collaborators. 

An ultra-sensitive platform

The new research investigates quantum sensing by leveraging color centers — small defects within crystals (Du’s team uses diamonds and other 2D layered materials) that allow light to be absorbed and emitted, which also give the crystal unique electronic properties. 

By embedding these color centers into a material called hexagonal boron nitride (hBN), the team hoped to create an extremely sensitive quantum sensor — a new resource for developing next-generation, transformative sensing devices. 

For its part, hBN is particularly attractive for quantum sensing and computing because it could contain defects that can be manipulated with light — also known as "optically active spin qubits."

The quantum spin defects in hBN are also very magnetically sensitive, and allow scientists to “see” or “sense” in more detail than other conventional techniques. In addition, the sheet-like structure of hBN is compatible with ultra-sensitive tools like nanodevices, making it a particularly intriguing resource for investigation.

The team’s research has resulted in a critical breakthrough in sensing spin waves, Du says, explaining that “in this study, we were able to detect spin excitations that were simply unattainable in previous studies.” 

Detecting spin waves is a fundamental component of quantum sensing, because these phenomena can travel for long distances, making them an ideal candidate for energy-efficient information control, communication, and processing.

The future of quantum

“For the first time, we experimentally demonstrated two-dimensional van der Waals quantum sensing — using few-layer thick hBN in a real-world environment,” Du explains, underscoring the potential the material holds for precise quantum sensing. “Further research could make it possible to sense electromagnetic features at the atomic scale using color centers in thin layers of hBN.”

Du also emphasizes the collaborative nature of the research, highlighting the diverse skill sets and resources of researchers within Georgia Tech. 

“Within the School of Physics, Professor Zhigang Jiang's research group provided the team with high-quality hBN crystals. Jingcheng Zhou, who is a member of both Professor Hailong Wang’s and my research teams, performed the cutting-edge quantum sensing measurements,” she says. “Many incredible students also helped with this project.”

Du is a leading scientist in the field of quantum sensing — this year, she received a new grant from the U.S. Department of Energy, along with a Sloan Research Fellowship for her pioneering work on developing state-of-the-art quantum sensing techniques for quantum information technology applications. The prestigious Sloan award recognizes researchers whose “creativity, innovation, and research accomplishments make them stand out as the next-generation of leaders in the fields.” 

DOI: 10.1126/sciadv.adk8495

This work is supported by the U. S. National Science Foundation (NSF) under award No. DMR-2342569, the Air Force Office of Scientific Research under award No. FA9550-20-1-0319 and its Young Investigator Program under award No. FA9550-21-1-0125, the Office of Naval Research (ONR) under grant No. N00014-23-1-2146, NASA-REVEALS SSERVI (CAN No. NNA17BF68A), and NASA-CLEVER SSERVI (CAN No. 80NSSC23M0229).