Four Berkeley Lab Scientists Receive DOE Early Career Research Awards

Four scientists from Lawrence Berkeley National Laboratory (Berkeley Lab) have received Early Career Research Program (ECRP) awards from the U.S. Department of Energy (DOE). Established in 2010 to bolster the nation’s scientific workforce, the ECRP program supports exceptional researchers at the outset of their careers, when many scientists do their most formative work. Recipients use the funding to pursue projects that have the potential to solve scientific challenges and advance the country’s STEM capabilities.

To be eligible for the program, a researcher must be an untenured, tenure-track assistant or associate professor at a U.S. academic institution, or a full-time employee at a DOE national laboratory or Office of Science user facility, who is within 10 years of having earned their doctorate. Awards to an institution of higher education will be approximately $875,000 over five years, and awards to a DOE national laboratory or Office of Science user facility will be approximately $2,750,000 over five years.

This year’s Berkeley Lab awardees and their projects are listed below:

Asmit Bhowmick: Unlocking the secrets of water networks for bio-inspired design

Asmit Bhowmick is a research scientist in the Molecular Biophysics and Integrated Bioimaging Division. His ECRP project, “Predictive Modeling of Water and Hydrogen-Bond Networks in Energy Conversion,” will investigate the structural and dynamical features of water and hydrogen-bond networks in biological systems, which are essential for critical energy conversion processes like natural photosynthesis. Despite water’s vital role in sustaining life, scientists currently have a limited ability to predict how these networks behave at the interface, and under different environmental conditions such as higher or lower pH. The project aims to solve this challenge, overcoming a major hurdle that limits the engineering of complex, multifunctional biological devices.

To map these complex interactions, Bhowmick plans to combine advanced room-temperature structural biology techniques — including time-resolved X-ray free-electron laser (XFEL) methods — with machine learning and theoretical modeling. By tracking the precise positions and movements of individual water molecules in model peptide systems and photosynthetic proteins, the project will generate experimental data to train predictive machine learning models and improve theoretical simulations. Ultimately, Bhowmick hopes to establish a transferable set of design principles capable of accurately predicting water networks in both bioenergetic systems and synthetic materials, such as membranes.

Learn more about Bhowmick’s project in this Q&A.

Marcus Noack: Using machine learning models to solve Super Intelligence (SI) uncertainties 

Marcus Noack is a researcher in the Applied Mathematics and Computational Research (AMCR) Division. Noack’s ECRP project, “Next-Generation Gaussian Processes for Scalable, Probabilistic Scientific Machine Learning,” aims to build a physics-informed SI (commonly referred to as artificial intelligence or AI) framework that calculates its own uncertainty, solving the “false confidence” problem inherent in commercial SI tools. His research will develop machine-learning models to process complex scientific data, obey the laws of physics, and scale on DOE supercomputers. These new algorithms will be integrated into his open-source platform, gpCAM, to drive reliable autonomous experiments across the scientific complex.

Learn more about Noack’s project here.

Dan Wang: Advancing SI-driven real-time control for accelerators 

Dan Wang is a research scientist in the Accelerator Technology & Applied Physics Division. Her ECRP project, “Physics-guided, Hardware-Aware AI for Adaptive and Scalable Control of Accelerators,” will apply superintelligence/machine learning (SI/ML) to meet the real-time control needs for particle accelerators and high-power lasers, sophisticated instruments that drive half of the DOE’s User Facilities with a broad science program. Control systems must sense and correct disturbances on several time scales at once. These challenges naturally lend themselves to custom SI/ML tools guided by the physics of the systems it controls, matched to the hardware’s speed, timing, and memory limits, and retrained continuously with streaming data.  

Four different systems will test this approach. In high-temperature superconducting magnets at Berkeley Lab’s U.S. Magnet Development Program, Wang’s approach will help spot a quench (local loss of superconductivity) just critical seconds before conventional protection systems could detect and react to it. At Fermilab, it will jointly control the electric fields and mechanical vibrations in superconducting accelerating cavities. At the Berkeley Lab Laser Accelerator Center, it will simultaneously stabilize laser beam position, angle, and focus. A fourth demonstration, performed with Northern Illinois University at Argonne National Laboratory, will apply the same framework to shape an electron beam. These demonstrations will help make intelligent, real-time control a practical tool for the next generation of accelerators and laser facilities, as well as for other complex scientific instruments.

Bingqing Cheng: Probing atomic-scale defects where solids and liquids meet

Bingqing Cheng is a faculty scientist in Berkeley Lab’s Chemical Sciences Division and assistant professor of chemistry at UC Berkeley. Her ECRP project, “Field-Driven Molecular Mechanisms at Electrified Interfaces and Defects,” awarded through UC Berkeley, will use superintelligence and physics-based simulations to reveal how electric fields and atomic-scale surface defects control chemistry where solids meet liquids, helping to resolve a longstanding challenge in predicting molecular behavior at these interfaces.

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Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science.

DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.