Skip to Content Skip to Search Skip to Utility Navigation Skip to Top Navigation
Los Alamos National Labs with logo 2021

STE Highlights, October 2022

Awards and Recognition

Zelenay wins 2022 IAGE Outstanding Researcher Award

Piotr Zelenay

Piotr Zelenay

Piotr Zelenay, researcher in the Materials Synthesis and Integrated Devices (MPA-11) group, has been honored by the International Association for Green Energy as the winner of the 2022 IAGE Outstanding Researcher Award. The IAGE supports green energy endeavors across academia, industry and society. The Outstanding Researcher Award honors recipients for “outstanding research and advancement of knowledge in fuel cells, electrochemical energy, and green energy systems” and is given to researchers who have “demonstrated exceptional contribution to the green energy research community.”

As a Laboratory Fellow and scientist, Zelenay concentrates on fundamental and applied aspects of polymer electrolyte fuel cell science and technology, electrocatalysis and electrode kinetics. His research has focused on electrocatalysis of oxygen reduction reaction and methanol and dimethyl ether oxidation in polymer electrolyte fuel cells, and, more recently, on electrochemical conversion of atmospheric carbon dioxide to value-added chemicals. He has taken a leading role in the development of non-precious metal oxygen reduction reaction catalysts worldwide; the discovery of ruthenium crossover in the direct methanol fuel cell; and the advancement of the direct dimethyl ether fuel cell performance to the level that now matches that of the state-of-the-art direct methanol fuel cells.

Zelenay has over 200 research publications to his credit, including in leading scientific journals such as Nature, Nature Catalysis, Science, Chemical Reviews, Accounts of Chemical Research, Angewandte Chemie, Energy and Environmental Science, and Advanced Materials. He has co-authored 26 patents and patent applications in the area of polymer electrolyte fuel cells. For his contributions to electrochemical science, Zelenay was awarded, among others, Fellowship of The Electrochemical Society in 2014 and Fellowship of the International Society of Electrochemistry in 2021. Zelenay joined Los Alamos National Laboratory and the fuel cell program in 1997. He received his doctoral and doctor of science degrees in chemistry from the University of Warsaw, Poland.

Return to top

Fellows selected for new graduate fellowship program with University of California, Irvine

Austin Green (left) and Marshall Campbell (right)

Austin Green (left) and Marshall Campbell (right)

The University of California, Irvine School of Physical Sciences and Los Alamos National Laboratory have partnered to create a new graduate fellowship program to support physical sciences students as they engage in collaborative research between the school and the Laboratory. The first two fellows are Marshall Campbell of the UCI department of physics and astronomy, and Austin Green of the UCI department of chemistry. Campbell works with UCI professor Luis A. Jauregui and will collaborate with researcher Michael Pettes at the Center for Integrated Nanotechnologies (CINT), while Green works with UCI professors Craig Martens and Shaul Mukamel, and will partner with Sergei Tretiak of the Los Alamos theoretical division. 

The fellowship is intended for those performing research in climate change and environmental systems science and engineering; renewable energy research, development and deployment; and materials and chemical research.

A fourth-year graduate student in the UCI physics and astronomy program, Campbell is also a graduate research assistant and GEM Fellow with CINT, working with Pettes since July 2021 on research that has so far yielded data for two publications still in preparation, related to strain engineering of two-dimensional material assemblies. Campbell’s fellowship project investigates the influence of strain and high magnetic fields on Dirac semimetals, a new class of topological quantum materials that possess strain-dependent properties. The project will specifically focus on HfTe5, a material having a strain response that should be measurable at relatively low magnetic fields in the sub-16 tesla range. If successful, these pilot experiments at cryogenic temperatures will open the door to examination of the response of such materials to much higher fields — up to 60 tesla, requiring microelectromechanical systems created using the microfabrication capabilities available in the Pettes laboratory, the CINT Core Integration Laboratory and at Los Alamos National Laboratory.

Green is a third-year graduate student who has coauthored one publication with Martens, while also working extensively with Mukamel. Martens and Green bring a new theoretical tool, Moyal Augmented Dynamics (MAD), to the existing collaboration between Mukamel and Tretiak. The MAD tool is capable of leveraging classical dynamics to model quantum coherences. Such coherences are important in the spectroscopic method called Transient Redistribution of Ultrafast Electronic Coherences in Attosecond Raman Signals (TRUECARS). With Green’s proposed research, the investigators will explore the applicability of the MAD formalism to the modeling of TRUECARS experiments that could be performed on X-Ray Free-Electron Laser facilities. Green will begin with previously studied molecular systems and ultimately focus his research on the design of coherently controlled quantum materials.

Return to top

Llobet named 2022 Impact Award winner

Anna Llobet Impact award

Llobet holds her Impact! Award at the October 8 awards ceremony. From left to right, Llobet’s partner Tracy Forward, daughter Gloria Galassi, Llobet and New Mexico Network for Women in Science and Engineering chair Katie Rodarte.

Anna Llobet has been named the recipient of the 2022 New Mexico Network for Women in Science and Engineering IMPACT! Award. The Impact! Award, established in collaboration with the New Mexico Commission on the Status of Women, honors New Mexico women scientists and engineers who have made impactful contributions through science, technology and engineering outreach; through mentoring; and through professional accomplishments.

Llobet began her career at the Laboratory as a postdoctoral fellow in 2001. She worked as a staff scientist in the Lujan Neutron Scattering Center, conducting research, training neutron scatterers and mentoring postdocs. Joining the Physics division, she used proton radiography to study dynamic materials behavior. With an extensive publication record, she joined the Surety and Safety group in the X Theoretical division in spring 2022.

Llobet’s recognition is also based on her leadership in the development of the Summer Physics Camp for Young Women in New Mexico. The camp started in 2017, supported through an American Physical Society minigrant and with the support of the New Mexico Consortium collaborative research partnership and the Laboratory’s Community Partnerships and Student Programs offices. The camp has grown from 20 students to 40 students per year and from Northern New Mexico to all of New Mexico and Hawaii and into an internship and pipeline program. The camp motivates young women to pursue higher education by providing hands-on activities and by exposing participants to role models in science, technology and engineering. Students also learn about the broad range of career opportunities available in Department of Energy national laboratories and in New Mexico. The Summer Physics Camp for Young Women is now part of a nationwide effort to expand similar efforts to other national laboratories locations.

The New Mexico Network for Women in Science and Engineering’s mission is to foster growth and empowerment for young girls through workshops related to science, technology and engineering and awards celebrating their technical and academic accomplishments.

Return to top

Burning Plasma Team earns John Dawson Award for Excellence in Plasma Physics Research

The National Ignition Facility was honored as the 2022 John Dawson Award for Excellence in Plasma Physics Research Recipient by the American Physical Society. The society’s annual honor is to “recognize a particular recent outstanding achievement in plasma physics research.” Specifically recognized was the Burning Plasma Team, which includes members from Los Alamos National Laboratory.

The National Ignition Facility was cited “for the first laboratory demonstration of a burning deuterium-tritium plasma where alpha heating dominates the plasma energetics.” The Burning Plasma Team contributed to achieving a burning plasma state in November 2020 and February 2021 at NIF, a critical step toward self-sustaining fusion — reactions in which the fusion generates more energy than it receives and can burn on its own. The achievement of burning plasma was described in Nature in January and further described in Physical Review Letters in August.

Researchers from Los Alamos on the Burning Plasma Team and an extension of that team known as the National Ignition Facility-Inertial Confinement Fusion team contributed essential capabilities and analysis for the experiments. Members on the Burning Plasma Team from Los Alamos include Kevin Meaney (P-4), Harry Robey (P-4) and Petr Volegov (P-1 retired). The NIF-ICF team’s Los Alamos members include Noah Birge (P-1), Valerie Fatherley (P-4), Hans Herrmann (M-DO), Yongho Kim (P-4), Hermann Geppert-Kleinrath (P-4), Verena Geppert-Kleinrath (P-1), John Kline (XTD-IDA), Tom Murphy (P-4 retired) and Carl Wilde (P-4). 

Return to top

Computer, Computational and Statistical Sciences

Novel container software accelerates science, ensures reliability and uptime

Containerized applications all have equal access to the system except their own software stacks.

Containerized applications all have equal access to the system except their own software stacks.

High performance computing systems at Los Alamos National Laboratory rely on Linux containers to isolate programs in separate computing environments, delivering a Lego-like, plug-and-play approach to software innovation. But the first Linux software containers, such as the popular open-source Docker container runtime, were not useable on Laboratory high performance computers due to security reasons, as Docker let users have administrator privileges to the system kernel, the heart of the operating system and the layer separating applications and hardware. The 2017 invention of Charliecloud, the lab’s powerful open-source code for building and using unprivileged HPC containers, has helped overcome this hurdle. The Charliecloud code means that new software can be applied to innovative science without sacrificing system time for rebooting and without compromising system reliability.

Developed by Timothy Randles, now a scientist in the Computer, Computational and Statistical Sciences division but then with the HPC Design group, and Reid Priedhorsky, staff scientist with the HPC Environments group, Charliecloud’s breakthrough insight is in using new Linux kernel features to let users bring their own software stack and deploy it on the system without ever crossing any privilege boundaries. Charliecloud creates completely unprivileged containers that use the supercomputer’s hardware without requiring administrator privileges, ensuring that the kernel continues to maintain the security of the system.

This transition to a new supercomputer user environment, enabled by Charliecloud, was partly facilitated by a 2019 paper in which Randles, Priedhorsky and Alfred Torrez in the HPC Design group showed that HPC containers have minimal or no impact on supercomputer performance, ensuring users get greater software flexibility with no loss in speed. Containers provide greater reliability in the multi-user environment of a national lab supercomputer. For example, when running a complex physics simulation, such as one depicting a supernova, computational scientists use a mix of their own code and that the supercomputer center supplies, often including dozens of libraries and compilers. In a shared environment, however, libraries often are updated and changed, something that users must track. Containers allow users and system administrators to keep their software separate.

Container technology has evolved beyond the user-system interface to become a systemwide approach to partitioning a supercomputer’s components. In this containerized configuration, a software package needs to only be updated in affected containers, which can then be restarted without rebooting the entire system or breaking a user’s application. The spread of the containerized approach means Department of Energy labs now have container efforts, and groups ranging from the Texas Advanced Computing Center to the University Corporation for Atmospheric Research have adopted Charliecloud to improve the balance between scientific innovation and system reliability.


“HPC Container Runtimes have Minimal or No Performance Impact,” 2019 IEEE/ACM International Workshop on Containers and New Orchestration Paradigms for Isolated Environments in HPC (CANOPIE-HPC), 37-42, (2019); DOI: 10.1109/CANOPIE-HPC49598.2019.00010. Authors: A. Torrez, T. Randles and R. Priedhorsky (Los Alamos National Laboratory).

Funding and mission

The work was supported by the DOE and NNSA Advanced Simulation and Computing program and the DOE’s Exascale Computing Project. The work supports the Global Security mission area and the Information, Science and Technology capability pillar.

Technical Contact: Timothy Randles (CCS-DO)

Return to top

Earth and Environmental Sciences

Study provides first comprehensive analysis of Ceboruco volcano seismic behavior

Ceboruco eruption history and parameters

Ceboruco eruption history and parameters, including eruptive volume, date, geochemistry and style. For comparison, Mount St. Helens’ 1980 eruption released approximately 1 cubic kilometer of material.

A new, detailed study of the Ceboruco volcano published in the Journal of Volcanology and Geothermal Research uses a dense, temporary network of 25 seismic stations to characterize aspects of the volcano’s seismicity, finding changes compared to earlier observations. Such changes may suggest resumption of activity at the currently dormant volcano. This collaboration between researchers at the University of Guadalajara and Los Alamos National Laboratory is the first time Ceboruco has been seismically observed with sufficient sensors to characterize the volcanic earthquake locations and magnitudes, and to distinguish earthquake sources beneath the volcano and its vicinity.

Ceboruco, in the northwestern Mexican state of Nayarit, is located 90 kilometers northeast of Puerto Vallarta, in the Trans-Mexican Volcanic Belt. It experienced its last eruptive period from 1870 to 1875 and is deemed the third-most hazardous volcano in Mexico. Active volcanoes generate many small earthquakes associated with magma, gas and fluids; monitoring these signals and noting important changes in them can be crucial to providing timely hazard assessments for dangerous volcanoes. The world’s active volcanoes are monitored by networks of geophysical instruments to better inform authorities regarding changing risk levels for infrastructure, air traffic, local residents and other stakeholders. Some volcanoes are well-monitored by established observatories and others are monitored inconsistently or not at all. Ceboruco, which with its active history poses an ongoing threat, has not had adequate surveillance. 

The seismic network of 25 seismic stations was set up over an area of 16 kilometers by 16 kilometers from November 2016 to July 2017. The effort detected 81 earthquakes, concentrated beneath the crater at a depth between 4 and 8 kilometers; the majority of events occurred in seismic swarms. The research team observed temporary migrations from northwest to southeast and from deeper to shallower depths. They uncovered new insights on local faults that follow the Tepic-Zacoalco rift, as well as ancient faults not associated with the rift, where the ascent of magmatic fluids could signal the volcano’s reactivation. The study’s results point to the necessity of a permanent seismic network on Ceboruco to provide real-time monitoring and short-term forecasting.

Funding and mission

This research was funded by the Mexican Center for Innovation in Geothermal Energy, the National Council of Science and Technology (Mexico) and the U.S. Department of Energy. The work supports the Energy Security mission area and the Complex Natural and Engineered Systems capability pillar.


“Recent seismicity at Ceboruco Volcano (Mexico),” Journal of Volcanology and Geothermal Research, 421, 107451 (2022); DOI: 10.1016/j.jvolgeores.2021.107451. Authors: Diana Núñez and Francisco J. Núñez-Cornú (University of Guadalajara); Charlotte A. Rowe (Los Alamos National Laboratory).

Technical contact: Charlotte Rowe (EES-17)

Return to top

Materials Science and Technology

Remote epitaxy for mismatched materials opens research pathways

Cross-sectional transmission electron microscopy image of ZnO/MoS2/ZnO heterostructure

Cross-sectional transmission electron microscopy image of ZnO/MoS2/ZnO heterostructure (left); cathodoluminescence spectrum of a single ZnO/MoS2/ZnO (right).

In the preparation of high-quality materials architecture, overcoming material compatibility challenges utilizing remote epitaxy — a technology for producing single-crystalline, free-standing thin films and structures — has been successful only with graphene. Less commonly studied, remote epitaxy for non-graphene, two-dimensional materials opens a pathway to the discovery of additional platforms for quantum materials research and novel optoelectronic devices. In recent research described in the journal ACS Nano, a team including researchers from the Center for Integrated Nanotechnologies and Los Alamos National Laboratory demonstrated that high-quality semiconductor nanostructures can be grown on an atomically thin molybdenum disulfide layer while maintaining structural integrity. The team also demonstrated that the atomically thin nanomaterials can be controlled in the new structures.

Epitaxy is an important technique in the preparation of high-quality semiconductor materials for device architectures and scientific research platforms. However, the atomically thin materials-based epitaxy has been limited to manufacturing because graphene, an atomically thin carbon layer, had been the only suitable material. The study demonstrated for the first time that epitaxy of conventional semiconductors is also available on other atomically thin materials — in this case, the conventional semiconductor zinc oxide on molybdenum disulfide. The accomplishment opens up a novel way to integrate different (incommensurate) materials in one architecture to deliver functionalities.

The functionality demonstrated in the study is enhanced light-matter interaction in the fabricated structure. The new epitaxial technique successfully fabricated a whispering-gallery-mode cavity composed of a single crystalline zinc oxide nanorod and monolayer molybdenum sulfide without structural defects. Light confinement in the cavity, showing enhanced luminescence of molybdenum sulfide and multimodal emission, was successfully observed. Calculations from first principles and cross-sectional transmission electron microscopy revealed that the novel heterostructure is formed by unconventional substrate-overlayer interaction; the heterostructure also exhibited lattice transparency. The team’s findings open up remote epitaxy applications beyond basic science to include advanced manufacturing possibilities.

Funding and Mission

This research was supported by the Laboratory Directed Research and Development program. The work supports the Energy Security mission area and the Materials for the Future capability pillar.


“Fabrication of a Microcavity Prepared by Remote Epitaxy over Monolayer Molybdenum Disulfide,” ACS Nano 16, 2399-2406 (2022); DOI: 10.1021/acsnano.1c08779. Authors: Jinkyoung Yoo, Yeonhoo Kim, John Watt (Center for Integrated Technologies, Los Alamos National Laboratory); Towfiq Ahmed (Los Alamos National Laboratory); Xueden Ma (Argonne National Laboratory); Suhyun Kim, Kibum Kang (Korea Advanced Institute of Science and Technology); Ting S. Luk (Center for Integrated Technologies, Sandia National Laboratory); Young Joon Hong (Sejung University).

Technical contact: Jinkyoung Yoo (MPA-CINT)

Return to top


Machine learning techniques used to model masses of atomic nuclei

The novel machine learning-based mass model’s predictive power is showcased for the neodymium isotopic chain.

The novel machine learning-based mass model’s predictive power is showcased for the neodymium isotopic chain. The model was trained on only three data points along this isotopic chain. It captures all trends found in known data (black triangles) and accurately predicts newly measured data (orange circles) that were also not included in training. Further, this model captures the one-, two- and three-sigma uncertainties indicated by the shaded bands.

Many thousands of atomic nuclei that have yet to be measured may exist in nature. As few as 2,500 atomic nuclei have been measured — their ground-state binding energies (or masses) are known. With two publications in a recent issue of Physical Review C, a Los Alamos National Laboratory team from the Theoretical division describes important advances in simulating the ground-state binding energy of atomic nuclei using a machine learning algorithm. Such an algorithm, the researchers determined, can find complex correlations in data, something theoretical nuclear physics models struggle to efficiently produce. The correlations captured by machine learning algorithms can provide insight for strengthening existing nuclear models.

In an article in Physical Review C selected as an Editor’s Suggestion, the Los Alamos research team shows for the first time that nuclear binding energies with quantified uncertainties may be reproduced with machine learning algorithms. The team used a probabilistic mixture density network to directly predict the masses across the chart of nuclides starting from a small subset of the available information in the 2016 Atomic Mass Evaluation, a recent compilation of the latest experimental nuclear data. The team extrapolated the inferred models beyond the experimental data in that data set. The research showed that the addition of physical information to the feature space increases the network’s accuracy. Instead of running a complicated physics model to produce predictions of the atomic nuclei’s ground-state binding energy, its mass can be simulated instead via the efficient machine learning algorithm. In so doing, the mixture density network is able to provide realistic uncertainty estimates for each predicted value, data that is harder to extract from modern nuclear physics models without burdensome computational expense.

The second, related Physical Review C Letter describes using the capacity of machine learning to encode complex correlations to tackle the difficult “many-body problem” in nuclear physics. The many-body problem speaks to the experience that the numerical complexity of directly modeling nuclear systems explodes combinatorially with increasing numbers of protons or neutrons. The Los Alamos team trained the machine learning model on a small fraction of the Atomic Mass Evaluation while introducing a novel physical constraint. The physical constraint ensures that the model obeys the known laws of physics when making predictions of unknown nuclei. The results showed that a machine-learning-based mass model can perform at parity with the best theoretical mass models when basic physical constraints are incorporated into the model. The key uncertainty measures the model is able to provide alongside its predictions offer value for extrapolating trends out to the limits of the nuclear landscape. Such models are crucial inputs for understanding the origin of the heaviest elements in astrophysical events. The team’s work has motivated some of the first experimental campaigns at the new DOE Facility for Rare Isotope Beams, which seeks to expand the known region of the nuclear chart.


“Nuclear masses learned from a probabilistic neural network,” Physical Review C, 106, 14305 (2022); DOI: 10.1103/PhysRevC.106.014305. Authors: A.E. Lovell, A.T. Mohan, T.M. Sprouse, and M.R. Mumpower (Los Alamos National Laboratory).

“Physically interpretable machine learning for nuclear masses,” Physical Review C, 106, L021301 (2022); DOI: 10.1103/PhysRevC.106.L021301. Authors: M. R. Mumpower, T. M. Sprouse, A. E. Lovell, and A. T. Mohan (Los Alamos National Laboratory).

Funding and mission

The work was funded by the Laboratory Advanced Scientific Computing (ASC) program and the Laboratory Directed Research and Development program. It supports the Global Security mission area and the Nuclear and Particle Futures capability pillar.

Technical Contact: Matthew Mumpower (T-2)

Return to top