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Berkeley Lab team helps develop 'AQuaRef' ai-quantum approach for protein structure modeling. March 11, 2026 Press play to listen to this content March 11, 2026 - Using a tool to solve a protein's structure, for most researchers in the world of structural biology and computational chemistry, is not unlike using the Rosetta Stone to unlock the secrets of ancient Egyptian texts. Once a protein's structure has been discovered, or defined, one can infer crucial information about its function or, in a diseased state, its dysfunction. While researchers have been pursuing the quest of solving protein structure for decades, advancing tools and computing technologies offer a new frontier for this work. A collaborative study recently published in Nature Communications unveiled a new computing program that offers a faster and more accurate way to determine protein structure at a new level of precision. Researchers from the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab), along with an international team of researchers, were a part of the effort. This tool, dubbed AI-enabled Quantum Refinement, or AQuaRef for short, uses quantum-mechanical calculations (QM) and artificial intelligence (AI) to predict the highly-accurate placement of atoms and electrons to determine a protein's molecular structure. This program is a part of Phenix, a comprehensive software suite that generates realistic computer models used by structural biologists around the world to solve macromolecular structures. "We're all basically a bunch of proteins," said Nigel Moriarty, a Berkeley Lab researcher and contributor to the recent publication. "They do so much in our bodies that detail the processes of life. Understanding their structure can give us insights into the mechanisms that cause disease in humans or produce energy in plants. All of this knowledge can lead to more effective therapeutics and bioenergy production." The current way of mapping a protein's structure entails bringing together two streams of information: experimental data produced through techniques like X-ray crystallography and cryogenic electron microscopy (cryo-EM), and theoretical data that exists in a library of detailed, known protein structural information. But the current options are limited, explained Moriarty, a computational research scientist in the Molecular Biophysics and Integrated Bioimaging (MBIB) Division's Phenix group. Our understanding today is limited to the chemical entities that have already been defined and doesn't yet include meaningful noncovalent interactions, the type of attraction typically seen holding a protein in its structural form. "That's where quantum and AI come in," he said. Nearly five years ago, members of the Phenix team began working with researchers at Carnegie Mellon University to explore how they might be able to apply their coding work to Phenix's offerings. The collaborative approach, coupled with 15 years of incremental research, led to this breakthrough program. In addition to Moriarty, other members of the Phenix team involved in this work were Paul Adams and Billy Poon, with Pavel Afonine leading the research. AQuaRef uses machine learning (ML) tools developed at Carnegie Mellon integrated with the Phenix software to compute energy and forces for scientifically interesting proteins - making quantum-level refinement practical where it was previously impossible. Of the 71 experiments that were tested in this study, AQuaRef produced higher quality structural information at a substantially lower computational cost while maintaining an equal or better fit to experimental data. In addition to the proof-of-concept results from this work, AQuaRef also correctly determined proton positions in DJ-1, a human protein linked to some forms of Parkinson's Disease, the structure of which has been notoriously difficult to map. Now that the team has confirmed that quantum-level refinement of a 3D protein model structure is possible, they're aiming to broaden the scope to include more diverse structures, such as those required for pharmaceutical drug design. And the potential impacts of this work reach far beyond human health, from better understanding the mechanisms of photosynthesis for enhanced crop productivity to mapping the proteins in plants as it relates to biofuel production. "There is a near-infinite number of things that can benefit from a detailed understanding of these mechanisms and protein structure," said Moriarty. "I'm excited to see how the paradigm shift that AQuaRef represents impacts the field of protein structure determination." This international team also included collaborators from the University of Wrocław, Poland, the University of Florida, and Pending.AI, Australia. This work was funded by the National Institutes of Health as well as with support from the Phenix Industrial Consortium. WASHINGTON, March 11, 2026 - Siemens today announced it has signed a Memorandum of Understanding (MOU)... Deep Engineering Collaboration on AI Factories, Powering Inference and Agentic AI, Enables Nebius to Deploy... ESPOO, Finland, March 11, 2026 - IQM Quantum Computers today announced the launch of Aalto Q20... TORONTO and DAEJEON, South Korea, March 11, 2026 - Xanadu Quantum Technologies Inc., a leading... Partnership expands IonQ's UK presence, accelerates IP generation, brings IonQ's world-class quantum networking capabilities, and... LOUISVILLE, Colo., March 11, 2026 - Infleqtion, a global leader in quantum computing and quantum sensing...

Berkeley Lab: 2026 CSA symposium helps researchers amplify their scientific impact. February 23, 2026 Press play to listen to this content Feb. 23, 2026 - This month, 13 early-career researchers from Berkeley Lab Computing Sciences Area (CSA) presented their work at the 2026 Postdoc Symposium, an event focused on articulating the real-world impact of their discoveries. More than just a showcase, the annual symposium is a launchpad for the next generation of scientific leaders. Through weeks of intensive coaching from CSA staff, participants hone their presentation skills and leave equipped with a professional recording of their talk to share with future employers. Since its inception in 2020, the program has helped shape 134 presentations, solidifying its role as a vital training ground for early-career researchers. "Our postdoctoral researchers represent the future of scientific innovation, and the CSA Postdoc Symposium is one of our most direct and impactful investments in their success. This program provides a unique platform not only to share their work but also to receive expert coaching and feedback that cultivates the essential communication skills they need to become leaders in their fields. These are the skills that will help them secure funding, build collaborations, and translate discovery into real-world solutions," said Stefan Wild, Director of Berkeley Lab's Applied Mathematics and Computational Research Division and a key champion of the program. Many of this year's participants echoed the value of the program: Alec Decktor "Having participated before, I knew the Postdoc Symposium was a fantastic event and the perfect venue to communicate my research progress to a broad Berkeley Lab audience. It's excellent practice for giving a non-technical talk - an invaluable skill for any scientist - and a great networking event. The connections I've made have led to exciting new research opportunities. "What makes the symposium unique is the opportunity to receive extremely valuable feedback on your presentation from senior scientists across different fields. This has directly helped me develop my ability to prepare talks for other conferences. To anyone who might be hesitant, I'd say the environment is incredibly supportive. The work you put in pays off directly; I've reused slides I created for the symposium in several other presentations. It's well worth the time and a great opportunity to develop yourself as an early-career scientist," said Alec Dektor, a postdoctoral researcher in Berkeley Lab's Scalable Solvers Group. Durga Mandarapu "The symposium process fundamentally changed how I approach presentations. The feedback from Lab leadership and communication experts was invaluable, and it helped me rethink how to design slides - for instance, learning to use the title to state the key takeaway instead of just a topic. That kind of clarity has a direct impact. When I later reached out to a Division Deputy who had provided feedback, he already had a clear understanding of my skills and research from the symposium. That familiarity made it much easier to identify opportunities for collaboration. It showed me that our work has more impact when people truly understand it, and this is the perfect place to learn that skill," said Durga Mandarapu, a postdoctoral researcher in Berkeley Lab's AMCR. Navjot Singh "What sets this symposium apart is that you're presenting to experts from a wide range of scientific disciplines, not just specialists in your own field. The feedback I received on how people outside my immediate area perceive certain concepts was invaluable - that's a perspective you don't easily get within your own research group. Learning to adjust my slides and delivery for that audience is a critical skill. It's an investment in one of the most important qualities of a successful scientist: the ability to communicate your work effectively across disciplinary boundaries," said Navjot Sing, a postdoctoral researcher in Berkeley Lab's AMCR. Alex Morehead "After seeing recordings of past events, I was convinced of the value of sharing my research at the Postdoc Symposium. The process of revising and presenting my research presentation has given me more confidence and knowledge for future presentations. The tight-knit community here at Berkeley Lab makes it an incredible place to connect with researchers interested in similar topics and gather relevant, valuable feedback. I'd encourage everyone to seriously consider it - practicing your presentation skills is a great long-term investment for any career," said Alex Morehead, 2025 Hopper Postdoctoral Fellow at NERSC. Shubhabrata Mukherjee "What makes the Postdoc Symposium so unique is how supportive and well-structured the entire experience is. The focus isn't just on presenting results; it's on helping you translate technically deep work for a broad audience in a collaborative environment. Condensing my research in AI and scientific data analysis clarified my thinking and built new collaborations across the Lab. It's a fantastic opportunity to gain confidence and practice a skill essential for any interdisciplinary career," said Shubhabrata Mukherjee, a Machine Learning Postdoctoral Fellow in AMCR. Nabin Giri "The symposium is an excellent experiential learning opportunity. The feedback from organizers and peers was incredibly helpful, teaching me to communicate my work with clarity and impact. It's the perfect preparation for job interviews and conferences because it gives you a safe space to practice the kind of communication that is essential for your career. The networking was terrific, and I made great connections and friends across the Lab," said Nabin Giri, a postdoctoral researcher in Berkeley Lab's Scientific Data Division (SciData), who is working on applying AI to structural biology. About Computing Sciences at Berkeley Lab High performance computing plays a critical role in scientific discovery. Researchers increasingly rely on advances in computer science, mathematics, computational science, data science, and large-scale computing and networking to increase our understanding of ourselves, our planet, and our universe. Berkeley Lab's Computing Sciences Area researches, develops, and deploys new foundations, tools, and technologies to meet these needs and to advance research across a broad range of scientific disciplines. 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How Berkeley Lab scaled synthesis 100x with Lab automation. Explore how Labman's system has been indispensable in this cutting-edge automated laboratory in California. Challenge and solution. With over 150,000 new materials developed by the Materials Project computers, Berkeley Lab, headquartered in California, could not synthesize these novel materials quickly enough to evaluate them for real-world applications. To overcome this, Berkeley Lab partnered with Labman to develop a fully customized system to synthesize the materials in an automated, twenty four hour a day operation pattern, guided by AI predictions for successful synthesis conditions and material composition. Labman technology has made a significant difference to this project. The system can now synthesize the novel materials in a safe and repeatable way in under an hour, a process that would have previously required about a day to achieve. Throughput was 100 times higher than that of manual material synthesis. 40 novel predicted compounds were produced in the first month, marking a significant advancement relative to traditional manual methods. Labman's clients are at the forefront of numerous areas of scientific research, especially in material science and formulation. For the researchers at Berkeley Lab (Lawrence Berkeley National Laboratory), manually synthesizing 150,000 novel AI-predicted materials presented a challenging task. With automation rapidly becoming an integral part of advanced laboratories worldwide, the research team turned to Labman to create a fully customized solution for their predicament. Using its extensive experience in handling diverse powders, liquid dispensing, formulation, and synthesis, Labman developed a high-throughput, powerful system to fulfill the project's strict requirements. The LBC system, based around a 6-axis robotic arm and equipped with a 'hotel' of dispensers handling more than 200 different powder-based materials, is ideal for creating distinct formulations. Labman's mechanical, electrical, and software engineers collaborated closely with the Berkeley Lab team to thoroughly understand the required process and ensure that it elevated their workflow. "The Labman team has been tremendously supportive and responsive throughout the whole project. I am grateful to work with Matt, Olly, Nick, and other team members at Labman to make and deliver our LBCS robot," stated Yan Zeng, a materials scientist who led Berkeley Lab's team on the project. "Labman has an open-minded attitude and great creativity with these talented and kind team members that I have not seen from another company," he said. An operator using LBCS, the automated formulation system for Berkeley Lab. Image Credit: Labman After system commissioning and initial testing were completed, Berkeley Lab researchers began processing the 150,000 proposed formulations for new materials listed by their systems. With speed and efficiency as core motivations behind the technology, the team was satisfied with the system's performance and the resulting materials created. I have made more new compounds in the last six weeks than my whole career. Gerbrand Ceder, Faculty Senior Scientist, LBNL Professor, UC Berkeley This exciting new technology opens up a world of possibilities for Berkeley Lab and the Material Project team. It marks the beginning of an excellent partnership with Labman, which will support the system and ensure the robot operates smoothly and efficiently for years to come. This information has been sourced, reviewed and adapted from materials provided by Labman. For more information on this source, please visit Labman.