Computers Math

New research shows that metasurfaces could be used as strong linear quantum optical networks This approach could eliminate the need for waveguides and other conventional optical components Graph theory is helpful for designing the functionalities of quantum optical networks into a single metasurfaceIn the race toward practical quantum computers and networks, photons — fundamental particles of light — hold intriguing possibilities as fast carriers of information at room temperature. Photons are typically controlled and coaxed into quantum states via waveguides on extended microchips, or through bulky devices built from lenses, mirrors, and beam splitters. The photons become entangled – enabling them to encode and process quantum information in parallel – through complex networks of these optical components. But such systems are notoriously difficult to scale up due to the large numbers and imperfections of parts required to do any meaningful computation or networking.Could all those optical components could be collapsed into a single, flat, ultra-thin array of subwavelength elements that control light in the exact same way, but with far fewer fabricated parts?
Optics researchers in the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) did just that. The research team led by Federico Capasso, the Robert L. Wallace Professor of Applied Physics and Vinton Hayes Senior Research Fellow in Electrical Engineering, created specially designed metasurfaces — flat devices etched with nanoscale light-manipulating patterns — to act as ultra-thin upgrades for quantum-optical chips and setups.
The research was published in Science and funded by the Air Force Office of Scientific Research (AFOSR).
Capasso and his team showed that a metasurface can create complex, entangled states of photons to carry out quantum operations – like those done with larger optical devices with many different components.
“We’re introducing a major technological advantage when it comes to solving the scalability problem,” said graduate student and first author Kerolos M.A. Yousef. “Now we can miniaturize an entire optical setup into a single metasurface that is very stable and robust.”
Metasurfaces: Robust and scalable quantum photonics processors

Their results hint at the possibility of paradigm-shifting optical quantum devices based not on conventional, difficult-to-scale components like waveguides and beam splitters, or even extended optical microchips, but instead on error-resistant metasurfaces that offer a host of advantages: designs that don’t require intricate alignments, robustness to perturbations, cost-effectiveness, simplicity of fabrication, and low optical loss. Broadly speaking, the work embodies metasurface-based quantum optics which, beyond carving a path toward room-temperature quantum computers and networks, could also benefit quantum sensing or offer “lab-on-a-chip” capabilities for fundamental science
Designing a single metasurface that can finely control properties like brightness, phase, and polarization presented unique challenges because of the mathematical complexity that arises once the number of photons and therefore the number of qubits begins to increase. Every additional photon introduces many new interference pathways, which in a conventional setup would require a rapidly growing number of beam splitters and output ports.
Graph theory for metasurface design
To bring order to the complexity, the researchers leaned on a branch of mathematics called graph theory, which uses points and lines to represent connections and relationships. By representing entangled photon states as many connected lines and points, they were able to visually determine how photons interfere with each other, and to predict their effects in experiments. Graph theory is also used in certain types of quantum computing and quantum error correction but is not typically considered in the context of metasurfaces, including their design and operation.
The resulting paper was a collaboration with the lab of Marko Loncar, whose team specializes in quantum optics and integrated photonics and provided needed expertise and equipment.
“I’m excited about this approach, because it could efficiently scale optical quantum computers and networks — which has long been their biggest challenge compared to other platforms like superconductors or atoms,” said research scientist Neal Sinclair. “It also offers fresh insight into the understanding, design, and application of metasurfaces, especially for generating and controlling quantum light. With the graph approach, in a way, metasurface design and the optical quantum state become two sides of the same coin.”
The research received support from federal sources including the AFOSR under award No. FA9550-21-1-0312. The work was performed at the Harvard University Center for Nanoscale Systems More

Precision cancer treatment on a larger scale is moving closer after researchers have developed an AI platform that can tailor protein components and arm the patient’s immune cells to fight cancer. The new method, published in the scientific journal Science, demonstrates for the first time, that it is possible to design proteins in the computer for redirecting immune cells to target cancer cells through pMHC molecules.
This dramatically shortens the process of finding effective molecules for cancer treatment from years to a few weeks.
“We are essentially creating a new set of eyes for the immune system. Current methods for individual cancer treatment are based on finding so-called T-cell receptors in the immune system of a patient or donor that can be used for treatment. This is a very time-consuming and challenging process. Our platform designs molecular keys to target cancer cells using the AI platform, and it does so at incredible speed, so that a new lead molecule can be ready within 4-6 weeks,” says Associate Professor at the Technical University of Denmark (DTU) and last author of the study Timothy P. Jenkins.
Targeted missiles against cancer
The AI platform, developed by a team from DTU and the American Scripps Research Institute, aims to solve a major challenge in cancer immunotherapy by demonstrating how scientists can generate target treatments for tumor cells and avoid damaging healthy tissue.
Normally, T cells naturally identify cancer cells by recognizing specific protein fragments, known as peptides, presented on the cell surface by molecules called pMHCs.It is a slow and challenging process to utilize this knowledge for therapy, often because the variation in the body’s own T-cell receptors makes it challenging to create a personalized treatment.
Boosting the body’s immune system
In the study, the researchers tested the strength of the AI platform on a well-known cancer target, NY-ESO-1, which is found in a wide range of cancers. The team succeeded in designing a minibinder that bound tightly to the NY-ESO-1 pMHC molecules. When the designed protein was inserted into T cells, it created a unique new cell product named ‘IMPAC-T’ cells by the researchers, which effectively guided the T cells to kill cancer cells in laboratory experiments.

“It was incredibly exciting to take these minibinders, which were created entirely on a computer, and see them work so effectively in the laboratory,” says postdoc Kristoffer Haurum Johansen, co-author of the study and researcher at DTU.
The researchers also applied the pipeline to design binders for a cancer target identified in a metastatic melanoma patient, successfully generating binders for this target as well. This documented that the method also can be used for tailored immunotherapy against novel cancer targets.
Screening of treatments
A crucial step in the researchers’ innovation was the development of a ‘virtual safety check’. The team used AI to screen their designed minibinders and assess them in relation to pMHC molecules found on healthy cells. This method enabled them to filter out minibinders that could cause dangerous side effects before any experiments were carried out.
“Precision in cancer treatment is crucial. By predicting and ruling out cross-reactions already in the design phase, we were able to reduce the risk associated with the designed proteins and increase the likelihood of designing a safe and effective therapy,” says DTU professor and co-author of the study Sine Reker Hadrup.
Five years to treatment
Timothy Patrick Jenkins expects that it will take up to five years before the new method is ready for initial clinical trials in humans. Once the method is ready, the treatment process will resemble current cancer treatments using genetically modified T cells, known as CAR-T cells, which are currently used to treat lymphoma and leukemia.Patients will first have blood drawn at the hospital, similar to a routine blood test. Their immune cells will then be extracted from this blood sample and modified in the laboratory to carry the AI-designed minibinders. These enhanced immune cells are returned to the patient, where they act like targeted missiles, precisely finding and eliminating cancer cells in the body. More

In an era where manipulated videos can spread disinformation, bully people, and incite harm, UC Riverside researchers have created a powerful new system to expose these fakes.
Amit Roy-Chowdhury, a professor of electrical and computer engineering, and doctoral candidate Rohit Kundu, both from UCR’s Marlan and Rosemary Bourns College of Engineering, teamed up with Google scientists to develop an artificial intelligence model that detects video tampering — even when manipulations go far beyond face swaps and altered speech. (Roy-Chowdhury is also the co-director of the UC Riverside Artificial Intelligence Research and Education (RAISE) Institute, a new interdisciplinary research center at UCR.)
Their new system, called the Universal Network for Identifying Tampered and synthEtic videos (UNITE), detects forgeries by examining not just faces but full video frames, including backgrounds and motion patterns. This analysis makes it one of the first tools capable of identifying synthetic or doctored videos that do not rely on facial content.
“Deepfakes have evolved,” Kundu said. “They’re not just about face swaps anymore. People are now creating entirely fake videos — from faces to backgrounds — using powerful generative models. Our system is built to catch all of that.”
UNITE’s development comes as text-to-video and image-to-video generation have become widely available online. These AI platforms enable virtually anyone to fabricate highly convincing videos, posing serious risks to individuals, institutions, and democracy itself.
“It’s scary how accessible these tools have become,” Kundu said. “Anyone with moderate skills can bypass safety filters and generate realistic videos of public figures saying things they never said.”
Kundu explained that earlier deepfake detectors focused almost entirely on face cues.

“If there’s no face in the frame, many detectors simply don’t work,” he said. “But disinformation can come in many forms. Altering a scene’s background can distort the truth just as easily.”
To address this, UNITE uses a transformer-based deep learning model to analyze video clips. It detects subtle spatial and temporal inconsistencies — cues often missed by previous systems. The model draws on a foundational AI framework known as SigLIP, which extracts features not bound to a specific person or object. A novel training method, dubbed “attention-diversity loss,” prompts the system to monitor multiple visual regions in each frame, preventing it from focusing solely on faces.
The result is a universal detector capable of flagging a range of forgeries — from simple facial swaps to complex, fully synthetic videos generated without any real footage.
“It’s one model that handles all these scenarios,” Kundu said. “That’s what makes it universal.”
The researchers presented their findings at the high ranking 2025 Conference on Computer Vision and Pattern Recognition (CVPR) in Nashville, Tenn. Titled “Towards a Universal Synthetic Video Detector: From Face or Background Manipulations to Fully AI-Generated Content,” their paper, led by Kundu, outlines UNITE’s architecture and training methodology. Co-authors include Google researchers Hao Xiong, Vishal Mohanty, and Athula Balachandra. Co-sponsored by the IEEE Computer Society and the Computer Vision Foundation, CVPR is among the highest-impact scientific publication venues in the world.
The collaboration with Google, where Kundu interned, provided access to expansive datasets and computing resources needed to train the model on a broad range of synthetic content, including videos generated from text or still images — formats that often stump existing detectors.
Though still in development, UNITE could soon play a vital role in defending against video disinformation. Potential users include social media platforms, fact-checkers, and newsrooms working to prevent manipulated videos from going viral.
“People deserve to know whether what they’re seeing is real,” Kundu said. “And as AI gets better at faking reality, we have to get better at revealing the truth.” More

On July 8, 2025, physicists from Aalto University in Finland published a transmon qubit coherence dramatically surpassing previous scientifically published records. The millisecond coherence measurement marks a quantum leap in computational technology, with the previous maximum echo coherence measurements approaching 0.6 milliseconds.
Longer qubit coherence allows for an extended window of time in which quantum computers can execute error-free operations, enabling more complex quantum computations and more quantum logic operations before errors occur. Not only does this allow for more calculations with noisy quantum computers, but it also decreases the resources needed for quantum error correction, which is a path to noiseless quantum computing.
“We have just measured an echo coherence time for a transmon qubit that landed at a millisecond at maximum with a median of half a millisecond,” says Mikko Tuokkola, the PhD student who conducted and analyzed the measurements. The median reading is particularly significant, as it also surpasses current recorded readings.
The findings have been just published in the prestigious peer-reviewed journal Nature Communications.
The researchers report their approach as thoroughly as possible, with the aim of making it reproducible for research groups around the world.
Finland cements position at forefront of quantum
Tuokkala was supervised at Aalto University by postdoctoral researcher Dr. Yoshiki Sunada, who fabricated the chip and built the measurement setup.

“We have been able to reproducibly fabricate high-quality transmon qubits. The fact that this can be achieved in a cleanroom which is accessible for academic research is a testament to Finland’s leading position in quantum science and technology,” adds Sunada who is currently working in Stanford University, USA.
The work is a result of the Quantum Computing and Devices (QCD) research group which is a part of Aalto University’s Department of Applied Physics, Academy of Finland Centre of Excellence in Quantum Technology (QTF), and the Finnish Quantum Flagship (FQF).
The qubit was fabricated by the QCD group at Aalto using high-quality superconducting film supplied by the Technical Research Centre of Finland (VTT). The success reflects the high quality of Micronova cleanrooms at OtaNano, Finland’s national research infrastructure for micro-, nano-, and quantum technologies.
“This landmark achievement has strengthened Finland’s standing as a global leader in the field, moving the needle forward on what can be made possible with the quantum computers of the future,” says Professor of Quantum Technology Mikko Möttönen, who heads the QCD group.
Scaling up the quantum computers of the future requires advancements across several domains. Among them are noise reduction, qubit-count increases, and the qubit coherence time improvements at the center of the new observations from the QCD. The group just opened a senior staff member and two postdocs positions for achieving future breakthroughs faster. More

A study by investigators at the Icahn School of Medicine at Mount Sinai, in collaboration with colleagues from Rabin Medical Center in Israel and other collaborators, suggests that even the most advanced artificial intelligence (AI) models can make surprisingly simple mistakes when faced with complex medical ethics scenarios.
The findings, which raise important questions about how and when to rely on large language models (LLMs), such as ChatGPT, in health care settings, were reported in the July 22 online issue of NPJ Digital Medicine[10.1038/s41746-025-01792-y].
The research team was inspired by Daniel Kahneman’s book “Thinking, Fast and Slow,” which contrasts fast, intuitive reactions with slower, analytical reasoning. It has been observed that large language models (LLMs) falter when classic lateral-thinking puzzles receive subtle tweaks. Building on this insight, the study tested how well AI systems shift between these two modes when confronted with well-known ethical dilemmas that had been deliberately tweaked.
“AI can be very powerful and efficient, but our study showed that it may default to the most familiar or intuitive answer, even when that response overlooks critical details,” says co-senior author Eyal Klang, MD, Chief of Generative AI in the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine at Mount Sinai. “In everyday situations, that kind of thinking might go unnoticed. But in health care, where decisions often carry serious ethical and clinical implications, missing those nuances can have real consequences for patients.”
To explore this tendency, the research team tested several commercially available LLMs using a combination of creative lateral thinking puzzles and slightly modified well-known medical ethics cases. In one example, they adapted the classic “Surgeon’s Dilemma,” a widely cited 1970s puzzle that highlights implicit gender bias. In the original version, a boy is injured in a car accident with his father and rushed to the hospital, where the surgeon exclaims, “I can’t operate on this boy — he’s my son!” The twist is that the surgeon is his mother, though many people don’t consider that possibility due to gender bias. In the researchers’ modified version, they explicitly stated that the boy’s father was the surgeon, removing the ambiguity. Even so, some AI models still responded that the surgeon must be the boy’s mother. The error reveals how LLMs can cling to familiar patterns, even when contradicted by new information.
In another example to test whether LLMs rely on familiar patterns, the researchers drew from a classic ethical dilemma in which religious parents refuse a life-saving blood transfusion for their child. Even when the researchers altered the scenario to state that the parents had already consented, many models still recommended overriding a refusal that no longer existed.
“Our findings don’t suggest that AI has no place in medical practice, but they do highlight the need for thoughtful human oversight, especially in situations that require ethical sensitivity, nuanced judgment, or emotional intelligence,” says co-senior corresponding author Girish N. Nadkarni, MD, MPH, Chair of the Windreich Department of Artificial Intelligence and Human Health, Director of the Hasso Plattner Institute for Digital Health, Irene and Dr. Arthur M. Fishberg Professor of Medicine at the Icahn School of Medicine at Mount Sinai, and Chief AI Officer of the Mount Sinai Health System. “Naturally, these tools can be incredibly helpful, but they’re not infallible. Physicians and patients alike should understand that AI is best used as a complement to enhance clinical expertise, not a substitute for it, particularly when navigating complex or high-stakes decisions. Ultimately, the goal is to build more reliable and ethically sound ways to integrate AI into patient care.”
“Simple tweaks to familiar cases exposed blind spots that clinicians can’t afford,” says lead author Shelly Soffer, MD, a Fellow at the Institute of Hematology, Davidoff Cancer Center, Rabin Medical Center. “It underscores why human oversight must stay central when we deploy AI in patient care.”

Next, the research team plans to expand their work by testing a wider range of clinical examples. They’re also developing an “AI assurance lab” to systematically evaluate how well different models handle real-world medical complexity.
The paper is titled “Pitfalls of Large Language Models in Medical Ethics Reasoning.”
The study’s authors, as listed in the journal, are Shelly Soffer, MD; Vera Sorin, MD; Girish N. Nadkarni, MD, MPH; and Eyal Klang, MD.
About Mount Sinai’s Windreich Department of AI and Human Health
Led by Girish N. Nadkarni, MD, MPH — an international authority on the safe, effective, and ethical use of AI in health care — Mount Sinai’s Windreich Department of AI and Human Health is the first of its kind at a U.S. medical school, pioneering transformative advancements at the intersection of artificial intelligence and human health.
The Department is committed to leveraging AI in a responsible, effective, ethical, and safe manner to transform research, clinical care, education, and operations. By bringing together world-class AI expertise, cutting-edge infrastructure, and unparalleled computational power, the department is advancing breakthroughs in multi-scale, multimodal data integration while streamlining pathways for rapid testing and translation into practice.

The Department benefits from dynamic collaborations across Mount Sinai, including with the Hasso Plattner Institute for Digital Health at Mount Sinai — a partnership between the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany, and the Mount Sinai Health System — which complements its mission by advancing data-driven approaches to improve patient care and health outcomes.
At the heart of this innovation is the renowned Icahn School of Medicine at Mount Sinai, which serves as a central hub for learning and collaboration. This unique integration enables dynamic partnerships across institutes, academic departments, hospitals, and outpatient centers, driving progress in disease prevention, improving treatments for complex illnesses, and elevating quality of life on a global scale.
In 2024, the Department’s innovative NutriScan AI application, developed by the Mount Sinai Health System Clinical Data Science team in partnership with Department faculty, earned Mount Sinai Health System the prestigious Hearst Health Prize. NutriScan is designed to facilitate faster identification and treatment of malnutrition in hospitalized patients. This machine learning tool improves malnutrition diagnosis rates and resource utilization, demonstrating the impactful application of AI in health care.
* Mount Sinai Health System member hospitals: The Mount Sinai Hospital; Mount Sinai Brooklyn; Mount Sinai Morningside; Mount Sinai Queens; Mount Sinai South Nassau; Mount Sinai West; and New York Eye and Ear Infirmary of Mount Sinai More

Research from the University of Minnesota Twin Cities gives new insight into a material that could make computer memory faster and more energy-efficient.
The study was recently published in Advanced Materials, a peer-reviewed scientific journal. The researchers also have a patent on the technology.
As technology continues to grow, so does the demand for emerging memory technology. Researchers are looking for alternatives and complements to existing memory solutions that can perform at high levels with low energy consumption to increase the functionality of everyday technology.
In this new research, the team demonstrated a more efficient way to control magnetization in tiny electronic devices using a material called Ni₄W-a combination of nickel and tungsten. The team found that this low-symmetry material produces powerful spin-orbit torque (SOT) — a key mechanism for manipulating magnetism in next-generation memory and logic technologies.
“Ni₄W reduces power usage for writing data, potentially cutting energy use in electronics significantly,” said Jian-Ping Wang, a senior author on the paper and a Distinguished McKnight Professor and Robert F. Hartmann Chair in the Department of Electrical and Computer Engineering (ECE) at the University of Minnesota Twin Cities.
This technology could help reduce the electricity consumption of devices like smartphones and data centers making future electronics both smarter and more sustainable.
“Unlike conventional materials, Ni₄W can generate spin currents in multiple directions, enabling ‘field-free’ switching of magnetic states without the need for external magnetic fields. We observed high SOT efficiency with multi-direction in Ni₄W both on its own and when layered with tungsten, pointing to its strong potential for use in low-power, high-speed spintronic devices.” said Yifei Yang, a fifth-year Ph.D. student in Wang’s group and a co-first author on the paper.

Ni₄W is made from common metals and can be manufactured using standard industrial processes. The low-cost material makes it very attractive to industry partners and soon could be implemented into technology we use everyday like smart watches, phones, and more.
“We are very excited to see that our calculations confirmed the choice of the material and the SOT experimental observation,” said Seungjun Lee, a postdoctoral fellow in ECE and the co-first author on the paper.
The next steps are to grow these materials into a device that is even smaller from their previous work.
In addition to Wang, Yang and Lee, the ECE team included Paul Palmberg Professor Tony Low, another senior author on the paper, Yu-Chia Chen, Qi Jia, Brahmudutta Dixit, Duarte Sousa, Yihong Fan, Yu-Han Huang, Deyuan Lyu and Onri Jay Benally. This work was done with Michael Odlyzko, Javier Garcia-Barriocanal, Guichuan Yu and Greg Haugstad from the University of Minnesota Characterization Facility, along with Zach Cresswell and Shuang Liang from the Department of Chemical Engineering and Materials Science.
This work was supported by SMART (Spintronic Materials for Advanced InforRmation Technologies), a world-leading research center that brings together experts from across the nation to develop technologies for spin-based computing and memory systems. SMART was one of the seven centers of nCORE, a Semiconductor Research Corporation program sponsored by the National Institute of Standards and Technology. This work is being supported by the Global Research Collaboration Logic and Memory program. This study was done in collaboration with the University of Minnesota Characterization Facility and the Minnesota Nano Center. More

Imagine trying to wear a left-handed glove on your right hand: it doesn’t fit because left and right hands are mirror images that can’t be superimposed on each other. This ‘handedness’ is what scientists call chirality, and it plays a fundamental role in biology, chemistry, and materials science. Most DNA molecules and sugars are right-handed, while most amino acids are left-handed. Reversing a molecule’s handedness can render a nutrient useless or a drug inactive and even harmful.
Light can also be left or right ‘handed’. When a light beam is circularly polarized, its electric field corkscrews through space in either a left-handed or right-handed spiral. Because chiral structures interact differently with these two types of twisted light beams, shining a circularly polarized light on a sample – and comparing how much of each twist is absorbed, reflected, or delayed – lets scientists read out the sample’s own handedness. However, this effect is extremely weak, which makes precise control of chirality an essential but challenging task.
Now, scientists from the Bionanophotonic Systems Laboratory in EPFL’s School of Engineering have collaborated with those in Australia to create artificial optical structures called metasurfaces: 2D lattices composed of tiny elements (meta-atoms) that can easily tune their chiral properties. By varying the orientation of meta-atoms within a lattice, scientists can control the resulting metasurface’s interaction with polarized light.
“Our ‘chiral design toolkit’ is elegantly simple, and yet more powerful than previous approaches, which tried to control light through very complex meta-atom geometries. Instead, we leverage the interplay between the shape of the meta-atom and the symmetry of the metasurface lattice,” explains Bionanophotonics Lab head Hatice Altug.
The innovation, which has potential applications in data encryption, biosensing, and quantum technologies, has been published in Nature Communications.
An invisible, dual layer watermark
The team’s metasurface, made of germanium and calcium difloride, presents a gradient of meta-atoms with orientations that vary continuously along a chip. The shape and angles of these meta-atoms, as well as the lattice symmetry, all work together to tune the response of the metasurface to polarized light.

In a proof-of-concept experiment, the scientists encoded two different images simultaneously on a metasurface optimized for the invisible mid-infrared range of the electromagnetic spectrum. For the first image of an Australian cockatoo, the image data were encoded in the size of the meta-atoms – which represented pixels – and decoded with unpolarized light. The second image was encoded using the orientation of the meta-atoms so that, when exposed to circularly polarized light, the metasurface revealed a picture of the iconic Swiss Matterhorn.
“This experiment showcased our technique’s ability to produce a dual layer ‘watermark’ invisible to the human eye, paving the way for advanced anticounterfeiting, camouflage and security applications,” says Bionanophotonics Systems Lab researcher Ivan Sinev.
Beyond encryption, the team’s approach has potential applications for quantum technologies, many of which rely on polarized light to perform computations. The ability to map chiral responses across large surfaces could also streamline biosensing.
“We can use chiral metastructures like ours to sense, for example, drug composition or purity from small-volume samples. Nature is chiral, and the ability to distinguish between left- and right-handed molecules is essential, as it could make the difference between a medicine and a toxin,” says Bionanophotonic Systems Lab researcher Felix Richter. More

Researchers have demonstrated a new technique that allows “self-driving laboratories” to collect at least 10 times more data than previous techniques at record speed. The advance – which is published in Nature Chemical Engineering – dramatically expedites materials discovery research, while slashing costs and environmental impact.
Self-driving laboratories are robotic platforms that combine machine learning and automation with chemical and materials sciences to discover materials more quickly. The automated process allows machine-learning algorithms to make use of data from each experiment when predicting which experiment to conduct next to achieve whatever goal was programmed into the system.
“Imagine if scientists could discover breakthrough materials for clean energy, new electronics, or sustainable chemicals in days instead of years, using just a fraction of the materials and generating far less waste than the status quo,” says Milad Abolhasani, corresponding author of a paper on the work and ALCOA Professor of Chemical and Biomolecular Engineering at North Carolina State University. “This work brings that future one step closer.”
Until now, self-driving labs utilizing continuous flow reactors have relied on steady-state flow experiments. In these experiments, different precursors are mixed together and chemical reactions take place, while continuously flowing in a microchannel. The resulting product is then characterized by a suite of sensors once the reaction is complete.
“This established approach to self-driving labs has had a dramatic impact on materials discovery,” Abolhasani says. “It allows us to identify promising material candidates for specific applications in a few months or weeks, rather than years, while reducing both costs and the environmental impact of the work. However, there was still room for improvement.”
Steady-state flow experiments require the self-driving lab to wait for the chemical reaction to take place before characterizing the resulting material. That means the system sits idle while the reactions take place, which can take up to an hour per experiment.
“We’ve now created a self-driving lab that makes use of dynamic flow experiments, where chemical mixtures are continuously varied through the system and are monitored in real time,” Abolhasani says. “In other words, rather than running separate samples through the system and testing them one at a time after reaching steady-state, we’ve created a system that essentially never stops running. The sample is moving continuously through the system and, because the system never stops characterizing the sample, we can capture data on what is taking place in the sample every half second.

“For example, instead of having one data point about what the experiment produces after 10 seconds of reaction time, we have 20 data points – one after 0.5 seconds of reaction time, one after 1 second of reaction time, and so on. It’s like switching from a single snapshot to a full movie of the reaction as it happens. Instead of waiting around for each experiment to finish, our system is always running, always learning.”
Collecting this much additional data has a big impact on the performance of the self-driving lab.
“The most important part of any self-driving lab is the machine-learning algorithm the system uses to predict which experiment it should conduct next,” Abolhasani says. “This streaming-data approach allows the self-driving lab’s machine-learning brain to make smarter, faster decisions, honing in on optimal materials and processes in a fraction of the time. That’s because the more high-quality experimental data the algorithm receives, the more accurate its predictions become, and the faster it can solve a problem. This has the added benefit of reducing the amount of chemicals needed to arrive at a solution.”
In this work, the researchers found the self-driving lab that incorporated a dynamic flow system generated at least 10 times more data than self-driving labs that used steady-state flow experiments over the same period of time, and was able to identify the best material candidates on the very first try after training.
“This breakthrough isn’t just about speed,” Abolhasani says. “By reducing the number of experiments needed, the system dramatically cuts down on chemical use and waste, advancing more sustainable research practices.
“The future of materials discovery is not just about how fast we can go, it’s also about how responsibly we get there,” Abolhasani says. “Our approach means fewer chemicals, less waste, and faster solutions for society’s toughest challenges.”
The paper, “Flow-Driven Data Intensification to Accelerate Autonomous Materials Discovery,” will be published July 14 in the journal Nature Chemical Engineering. Co-lead authors of the paper are Fernando Delgado-Licona, a Ph.D. student at NC State; Abdulrahman Alsaiari, a master’s student at NC State; and Hannah Dickerson, a former undergraduate at NC State. The paper was co-authored by Philip Klem, an undergraduate at NC State; Arup Ghorai, a former postdoctoral researcher at NC State; Richard Canty and Jeffrey Bennett, current postdoctoral researchers at NC State; Pragyan Jha, Nikolai Mukhin, Junbin Li and Sina Sadeghi, Ph.D. students at NC State; Fazel Bateni, a former Ph.D. student at NC State; and Enrique A. López-Guajardo of Tecnologico de Monterrey.
This work was done with support from the National Science Foundation under grants 1940959, 2315996 and 2420490; and from the University of North Carolina Research Opportunities Initiative program. More