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News — LOS ALAMOS, N.M., Aug. 7, 2023 — In a banner year for Los Alamos National Laboratory in the competition for Department of Energy Early Career Research Awards, four scientists won multiyear funding for their projects.

“Early-career scientists are the lifeblood of the Lab’s future, so it is particularly gratifying to see these four researchers acknowledged by the DOE awards,” said Laboratory Director Thom Mason. “Congratulations go to Rich, Kun, Andrey and Yu as they carry out these outstanding projects.”

The winners and their research projects are:

• Rich Fiorella, improving modeling of coastal-urban environmental change using stable water isotope ratios and numerical tracers.
• Kun Liu, understanding the origin of the hadron mass.
• Andrey Lokhov, improving the mathematics underpinning machine learning for many-body quantum physics, power grids, turbulence and field theories.
• Yu Zhang, boosting multiscale modeling capabilities for molecular quantum electrodynamics on DOE exascale computers.

Rich Fiorella: Probing water cycle processes and extremes in coastal and urban environments using water isotope ratio tracers and numerical tags.

This project develops a new comprehensive water tracking system throughout the Department of Energy’s Energy Exascale Earth System Model (E3SM) that includes stable water isotope ratios, a common geochemical signature used to understand water cycle processes. The research addresses limitations in representing coastal and urban systems in Earth system models by developing new ways to connect observations with simulations, and by providing enhanced process-level understanding of hydrological processes and the complex feedbacks between urbanization and coastal and environmental change.

Connecting this new tracking system to the existing capabilities will allow researchers to use the enhanced E3SM to study coastal change, extreme event susceptibility, urbanization impacts on precipitation and flooding and potential solutions for increasing coastal city resilience. This research was selected for funding by the Office of Biological and Environmental Research.

“I’m really excited about the early career award as it will allow me to apply the tools of isotope ratios and water tracers in DOE’s flagship earth system model, allowing us to understand changes in coastal and urban systems, and potentially allowing us to use the model as a testbed for increasing coastal city resilience,” Fiorella said.

Fiorella is a staff scientist in the Earth and Environmental Sciences division at Los Alamos. He earned undergraduate degrees in chemistry and environmental studies from the University of Pittsburgh and a master’s in earth and environmental science and Ph.D. in geology from the University of Michigan-Ann Arbor. His research interests at Los Alamos center on using stable water and carbon isotope ratios to understand changes in the Earth’s water and carbon cycles and the applications of these tracers and impacts of these cycles on ecosystems, society and environmental security. 

Kun Liu: Probing the emergent hadron mass through pion structure measurement at the AMBER experiment.

Understanding the origin of the hadron mass, which constitutes 99% of our visible universe, is one of the central goals of nuclear physics. The vast majority of the mass is believed to come from the strong force that tightly binds quarks and gluons together, so measuring the quark and gluon structure of the hadron will help explain how the hadron mass emerges through the strong interaction. In contrast to the abundant data on the proton structure, data on the pion structure are very scarce and outdated. This project will study the internal structure of the pion by colliding high-energy pions at the AMBER experiment at CERN by developing a detector to improve the existing AMBER spectrometer. That will enable the best-ever measurement of the pion structure, helping researchers understand and constrain the mechanisms leading to the emergence of the hadron mass.

A nuclear physicist, Liu joined Los Alamos as a graduate research assistant, received his Ph.D. from Peking University in China, became a Laboratory postdoc, and then converted to a staff scientist. His research interests center on understanding the inner structure of the nucleon. Since joining the Laboratory, he has primarily worked on the fixed target Drell-Yan experiments at Fermilab, probing the internal structure of the proton.

“Receiving the DOE Early Career Award is an incredible honor, propelling our research on the internal structure of the pion and expanding our comprehension to shed light on the fundamental origins of our visible universe,” Liu said.

Andrey Lokhov: Resurgence of Markov Random Fields for Scientific Machine Learning: New Mathematics for an Old Framework.

This project aims at eliminating known barriers in scientific machine learning by advancing the mathematics for a general class of probability distributions known as Markov Random Fields (MRFs). The work addresses the challenge of designing a suite of efficient learning algorithms that incorporate physical symmetries, deal with heterogeneous and noisy data sets and construct MRFs in a form that allows for generation of predictions and sampling.

To demonstrate a wide applicability of methods, the research focuses on problems in several scientific areas that require rigorous learning of interpretable and physics-informed probabilistic network models from distributed data: many-body quantum physics, power grids, turbulence and field theories. This project was selected for funding by the Advanced Scientific Computing Research program office.

“The early career award is unique because it supports a long-term vision and enables a continuity of research over a period of five years,” Lokhov said. “This significant investment in learning of high-dimensional probability distributions will allow us to develop the full potential of promising early results in scientific machine learning obtained with colleagues from Theoretical division.”

Lokhov is a staff scientist in Theoretical division at Los Alamos. He holds a Ph.D. in statistical physics from Université Paris-Sud and a master’s in theoretical physics from École Polytechnique and École Normale Supérieure. His long-standing interests include statistical learning algorithms, inference and optimization problems on networks, and quantum computing. He has been the principal investigator for multiple projects on physics-informed machine learning at Los Alamos.

Yu Zhang: Multiscale Ecosystem for Molecular Quantum Electrodynamics.

Controlling chemistry and molecular properties with light has become possible via the formation of quantum quasiparticles, namely polaritons, through strong coupling between molecules and the quantized radiation field inside an optical cavity. Theoretical on-the-fly simulations of polariton dynamics are essential to understand these hybrid light-matter systems. However, the complexity of these systems, marked by multiple coherent and dissipative interactions across different time and length scales, makes modeling a significant challenge.

In response to this multiscale challenge, this project is developing a comprehensive, scalable, multiscale ecosystem that bridges quantum optics, computational electromagnetics and quantum chemistry. The aim is to deliver advanced multiscale modeling capabilities for describing self-consistent interactions in light-matter hybrid systems within the rigorous framework of molecular quantum electrodynamics on DOE exascale computers. This research was selected for funding by the Office of Basic Energy Sciences.

“I am deeply honored and grateful to receive the DOE Early Career Award,” Zhang said. “This funding will empower us to advance multiscale modeling, paving the way to groundbreaking understanding and manipulation of light-matter interactions with broad implications across numerous scientific disciplines.”

Zhang is a staff scientist in Theoretical division at Los Alamos. He received a bachelor’s in physics from Sun Yat-sen University and his Ph.D. in chemical physics from the University of Hong Kong. Yu is interested in developing and applying theoretical models and computational methods in various areas of chemical physics, including quantum transport, quantum dissipation, open-quantum systems, non-adiabatic dynamics and light-matter interactions.

About the Early Career Research Program

Researchers in universities and DOE national laboratories compete for awards under the DOE Office of Science Early Career Research Program. The funds support outstanding early-career scientists, stimulating their careers in the disciplines funded through Office of Science programs.

For more information, visit the page.

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