STE Highlights, February 2024
Awards and Recognition
Materials Physics and Applications
Indicators of topological properties in kagome metal revealed using high magnetic fields
Materials Science and Technology
Chemistry of Materials acclaims Lab’s machine-learning-guided materials research
Physics
Los Alamos team models strongest material in the universe
Updated physics framework allows for simultaneous analysis of colliding neutron star signals
Building hard physics constraints into deep convolutional neural networks for magnetohydrodynamics
Awards and Recognition
Osmieri receives young investigator award from Italian foundation
Luigi Osmieri
Luigi Osmieri of the Materials Synthesis and Integrated Devices (MPA-11) group at Los Alamos National Laboratory received the 2023 Franco Strazzabosco Award for Research in Engineering with Focus on Sustainable Energy from the Italian Scientists and Scholars in North America Foundation (ISSNAF).
The award honors the entrepreneurial courage of Italian engineers who seek to apply scientific discoveries to public benefit. Osmieri was recognized for his work advancing catalysts and electrodes for affordable hydrogen generation and utilization. The prize is one of six ISSNAF young investigator awards presented to outstanding early career Italian researchers working in the United States and Canada.
Osmieri, who received his doctorate in chemical engineering from the Politecnico di Torino, Italy, joined the Laboratory in 2020, after completing a postdoctoral appointment at the National Renewable Energy Laboratory. There he was the runner-up for the DOE Hydrogen and Fuel Cell Technologies Office’s Postdoctoral Recognition Award.
He is a former Director’s Postdoctoral Research Fellow and now a staff scientist in the Fuel Cell and Electrochemical Sensors team. He is the author of 38 publications, with more than 1,800 citations and an H-index of 24. His main research interests are precious metal-free electrocatalysts, electrode engineering and electrochemical diagnostics applied to low-temperature electrochemical energy conversion devices.
Capability Enhancement
New technology revolutionizes glove box decontamination
Researchers Rami Batrice (B-TEK) and Janelle Droessler (MPA-11) demonstrate the application of the MENDS technology for glove box decontamination, highlighting bespoke suction heads designed for the unique geometries of radiological glove boxes. Credit: Los Alamos National Laboratory
Chemistry, Engineering and Bioscience divisions at Los Alamos National Laboratory recently deployed a novel system for surface decontamination. The Los Alamos-developed Modular Electrochemical Nuclear Decontamination System (MENDS) modernizes surface decontamination — increasing efficiency, reducing costs and improving worker safety. MENDS is currently supporting glove box decontamination and decommissioning efforts at TA-55 in support of the Laboratory’s plutonium pit production mission.
Thousands of glove boxes within the Department of Energy and national laboratory system are slated for decontamination and decommissioning. Most of the glove boxes have been used in radiological facilities and reach transuranic waste contamination levels, requiring disposition at the Waste Isolation Pilot Plant (WIPP), the sole U.S. repository for highly radioactive waste. MENDS revolutionizes the decontamination process and removes the surface radionuclides down to low-level waste criteria, allowing for disposal as general laboratory waste instead of disposal at WIPP.
MENDS is expected to save tens of millions of dollars required for disposal of decommissioned radiological glove boxes by the Laboratory alone, with billions in savings estimated for the DOE complex at large.
MENDS incorporates a Los Alamos-developed electrochemical flow cell into a closed-loop, recirculating system, with a series of bespoke suction heads designed to suit a range of applications. This creates a fixed‑volume system that minimizes waste produced during the decontamination process. MENDS is a semiautonomous system that drastically reduces decontamination time, thereby significantly reducing worker radiation exposure.
MENDS leverages established chemistry principles discovered in the late 1970s by Pacific Northwest National Laboratory, in collaboration with Exxon Corporation, that were intended for the dissolution and processing of refractory metal oxides but suffered from a lack of scalability. The Los Alamos‑developed flow cell solves the previous restrictions on the scale of the chemical reaction, enabling infinite scalability with a finite-volume of decontamination solution. MENDS’ proprietary flow cell outperforms competing technologies and commercially available flow cells while being about half the size and 20 times lighter, with significantly improved chemical resistance — all while easily adapting to unique user needs.
The modular components and adaptability of MENDS offer solutions to a variety of problems across various industries. Iterations of the system have been developed and are currently being tested for a wide array of government and industrial applications, including decontamination of radiothermal generators used by NASA in several space exploration devices, decontamination of spent nuclear fuel tubes and recovering rare earth metals in urban mining applications.
Funding and mission
The work was supported by the National Nuclear Security Administration, Office of Defense Program’s Production Modernization Office, Plutonium Program Office and the Laboratory Directed Research and Development program at Los Alamos. The work supports the Nuclear Deterrence mission area and the Nuclear and Particle Futures capability pillar.
Technical contact: Rami Batrice (B-TEK)
Materials Physics and Applications
Indicators of topological properties in kagome metal revealed using high magnetic fields
For each field pulse, a tiny crystal of CsV3Sb5 was rotated to different precise angles for each pulse. The angles are shown at right. Wiggles are seen in the frequency, and their amplitude varies with the crystal’s angle in a distinctive way. Image published in Communications Materials and used via Creative Commons license.
The recently discovered kagome metal CsV3Sb5 has attracted considerable attention because it is thought to host small Chern Fermi pockets that possess spontaneous orbital currents and large orbital magnetic moments. Chern pockets are a key indicator of a quantum mechanical property known as topology, which promises to be invaluable in future electronic devices that will work on completely new quantum principles. In work described in Communications Materials, a research team probed the material’s properties using high magnetic fields generated at Los Alamos’ Pulsed Field Facility of the National High Magnetic Field Laboratory.
Until the present measurement, the presence of Chern Fermi pockets in CsV3Sb5 has proven impossible to definitively detect because the pockets align antiferromagnetically. That is, Chern pockets with oppositely circulating currents pair up, cancelling out their magnetic fields. Studying CsV3Sb5, under pulsed magnetic fields of up to 75 Tesla, the experiment recorded the electrical conductivity of CsV3Sb5 via its effect on the frequency f of an oscillator. For each field pulse, a tiny crystal of CsV3Sb5 was rotated to different precise angles; wiggles are seen in the frequency, their amplitude varying with the crystal’s angle in a distinctive way.
The high fields cause electrons to tunnel out of the Chern pockets onto more conventional bands in CsV3Sb5 and then back again; this repetitive back-and-forth motion leads to the wiggles. Because the two types of Chern pocket have opposite currents, their tunneling processes are not quite the same, producing two sets of wiggles with slightly different frequencies. These frequencies vary differently as the angle of the crystal in the field changes; as a result, the two sets of wiggles go in and out of phase, leading to an overall amplitude that goes up and down. It is this variation of the wiggle amplitude — visible in the raw data — that definitively identifies the orbital moments of the Chern pockets. This visible manifestation is a remarkable example of the interplay between topological effects and more conventional electronic bands in quantum materials, giving hope for future devices in which topology is writ large.
Funding and mission
This work is supported by the National Science Foundation; the U.S. Department of Energy, including the Basic Energy Sciences office, and including the Basic Energy Sciences program “Science at 100 T”; the Institute for Complex Adaptive Matter; and the Gordon and Betty Moore Foundation. The work supports the Global Security mission area and the Materials for the Future capability pillar.
Reference
“Magnetic breakdown and spin-zero effect in quantum oscillations in kagome metal CsV3Sb5,” Communications Materials, 4 (2023); DOI: 10.1038/s43246-023-00422-y. Authors: Kuan-Wen Chen, Guoxin Zheng, Dechen Zhang, Aaron Chan, Yuan Zhu, Kaila Jenkins and Lu Li (University of Michigan); Fanghang Yu, Mengzhu Shi, Jianjun Ying and Xianhui Chen (University of Science and Technology of China); Ziji Xiang (University of Michigan, University of China); Ziqiang Wang (Boston College); and John Singleton (Los Alamos National Laboratory).
Technical contact: John Singleton (MPA-MAGLAB)
Materials Science and Technology
Chemistry of Materials acclaims Lab’s machine-learning-guided materials research
414 compositions are identified as the most promising candidates for future experimental synthesis of novel oxide perovskites. Image from Chemistry of Materials.
A team of Los Alamos materials scientists has received an honorable mention for best paper from Chemistry of Materials. The award recognizes “A machine learning approach for the prediction of formability and thermodynamic stability of single and double perovskite oxides,” by Anjana Talapatra, Blas Uberuaga and Ghanshyam Pilania (MST-8), and Christopher Stanek (MST-DO). Their manuscript outlines a machine learning technique to identify novel chemistries in perovskites that may lead to their enhanced functionality.
Perovskite oxides are attractive due to their wide-ranging electrical and magnetic properties, which are tunable for diverse applications. To identify novel compositions likely to form stable compounds, the researchers employed a systematic computational screening strategy to the exhaustive chemical space of single and double perovskites.
Leveraging Lab expertise in atomistic calculations, the researchers developed training data sets that were used to build highly accurate machine learning classification models, which in turn were used to screen for novel stable oxide perovskites. Of the 5.19 million candidates, their technique identified 0.89 million formable and 0.43 million stable double perovskites. The novelty of the new compounds alone demonstrates the utility of this approach and opens new avenues for materials design for advanced applications.
The researchers will present their work in a special invited session at the American Chemical Society meeting in March 2024.
Funding and mission
This research was supported by Los Alamos’ Laboratory Directed Research and Development Program, with computational support from the Lab’s high-performance computing clusters. The work supports the Lab’s Energy Security mission and Materials for the Future capability pillar.
Reference
“A machine learning approach for the prediction of formability and thermodynamic stability of single and double perovskite oxides,” Chemistry of Materials, 33, 3, 845-858 (2021); DOI: 10.1021/acs.chemmater.0c03402. Authors: Anjana Talapatra, Blas Uberuaga, Ghanshyam Pilania, Christopher Stanek (Los Alamos National Laboratory).
Technical contact: Anjana Talapatra (MST-8)
Physics
Los Alamos team models strongest material in the universe
A time snapshot of tangential velocity in the neutron-star crust during simulated toroidal oscillations. The crust material is a solid with a shear modulus of 1020 gigapascals (GPa); in comparison, the shear modulus of steel is about 75 GPa. One oscillation of the crust takes about 50 milliseconds. Comparable oscillations of Earth take tens of minutes. Image from The Astrophysical Journal Supplement Series and used via Creative Commons license.
Neutron stars hold mysteries about the nature of dense nuclear matter, cosmic explosions and the production of heavy elements in the universe. Scientists at Los Alamos National Laboratory are finding new ways to explore these mysteries by developing and using state-of-the art numerical tools. One of them is a computer code which, for the first time, can model the three-dimensional motion and breaking of the crystalline neutron-star crust.
This outer layer in neutron stars contains the strongest material known in nature. An intriguing question is what will happen to the crust when two neutron stars are in a binary spiral toward a merger. Specifically, will the crust crack or affect the detected gravitational-wave signal from a merger? Previous simulation codes either described the crust as a fluid or were not able to model its dynamical evolution in three dimensions. They could therefore not fully answer these questions.
Inspired by the first gravitational-wave detection of a neutron star merger (GW170817), a team of scientists from the CCS and T divisions leveraged an existing Los Alamos code, FleCSPH, to include the modeling of the solid neutron-star crust. Their work, described in The Astrophysical Journal Supplement Series and recapped in AAS Nova, contains the first dynamic simulations of the toroidal motion of the solid crust that might be observed in powerful X-ray flares. This is an important first step toward including the solid crust in neutron-star merger simulations.
Based on the results from the Los Alamos team’s efforts, the Center for Space and Earth Sciences within the Laboratory’s National Security Education Center is supporting a postbaccalaureate fellowship for follow-up work. The project’s goal is to explore signatures of a potential solid neutron-star core made of quark matter, which is also studied in the Relativistic Heavy-Ion Collider as well as the Large Hadron Collider on Earth.
FleCSPH is an open-source smoothed particle hydrodynamics code built on top of the Flexible Computational Science Infrastructure (FleCSI) that was developed as part of the Laboratory’s Ristra Project. The code was initially written by CCS scientists Julien Loiseau and Hyun Lim when they were participants in the Laboratory’s 2016 Co-Design Summer School.
Funding and mission
The work, which supports the Laboratory’s Global Security mission and its Nuclear and Particle Futures pillar, was funded by a Laboratory Directed Research and Development grant for Exploratory Research and the Laboratory’s Institutional Computing program.
Reference
“Modeling Solids in Nuclear Astrophysics with Smoothed Particle Hydrodynamics,” The Astrophysical Journal Supplement Series 267, 47 (2023); DOI: 10.3847/1538-4365/acdc94. Authors: Irina Sagert, Oleg Korobkin, Ingo Tews, Hyun Lim, Micael Falato, Julien Loiseau (Los Alamos National Laboratory); Bing-Jyun Tsao (University of Texas at Austin).
Technical contact: Irina Sagert (CCS-2)
Updated physics framework allows for simultaneous analysis of colliding neutron star signals
The blue lines represent possible equations of state constrained by quantum Monte Carlo calculations using chiral effective field theory interactions and extended to higher densities using a speed of sound model. Different astrophysical messengers can then constrain the equations of state’s different regions. Indicated by rectangulars, the different astrophysical messengers include gravitational waves from inspirals of neutron star mergers, data from radio and X-ray pulsars, and electromagnetic signals associated with neutron star mergers. The boundaries are not strict but depend on the equations of state and properties of the studied system. Image originally published in Nature Communications and used via Creative Commons license.
Theoretical division scientists Ingo Tews and Rahul Somasundaram (T-2) are part of an international interdisciplinary team that has developed an extension of the nuclear-physics and multi-messenger astrophysics, or NMMA, framework. Published in Nature Communications, the code was able to simultaneously analyze three distinct signals — the gravitational-wave signal, the kilonova and the gamma-ray burst afterglow — from colliding neutron stars, a scientific breakthrough. The results allowed the team to estimate, to within 3%, the radius of a 1.4 solar mass neutron star.
Neutron stars are the densest stellar remnants that can be directly observed in the universe, and their collisions are some of the most violent cosmic events. Robust theoretical models help to reliably describe the dense matter in these environments, and subsequently the gravitational-wave and electromagnetic emissions from these events. The NMMA framework allows scientists to robustly infer the models from the observational data. The extension of the NMMA code described in Nature Communications represents a first attempt at a simultaneous analysis of the three distinct signals. Scientists hope to use this computational framework to learn about nuclear physics in neutron stars, leading to a better understanding of interactions in atomic nuclei and how and where atomic nuclei are created; in essence, scientists can gain more knowledge about the smallest building blocks of matter from some of the most explosive astrophysical events.
The NMMA code allows incorporation of low-density physics constraints, calculated in the Theoretical division at the Laboratory, as well as X-ray and radio observations of isolated neutron stars. The code had previously been employed to study supranuclear dense matter and the Hubble constant (an observation of the universe’s expansion). The code has also proved useful in classifying electromagnetic observations, performing model selection and comparing dense-matter physics probed in neutron star mergers. Development of the NMMA as a tool will allow researchers to extract as much information as possible from astrophysical events. These events serve as a “cosmic laboratory” for probing physics under conditions that cannot be achieved in experiments on Earth.
Funding and mission
The work was supported by the National Science Foundation and by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics, and the Office of Advanced Scientific Computing Research, Scientific Discovery through Advanced Computing program; and the Laboratory Directed Research and Development program at Los Alamos. Computational resources were provided by the Institutional Computing program at Los Alamos and by the National Energy Research Scientific Computing Center, supported by the U.S. Department of Energy, Office of Science. The work supports the Nuclear and Particle Futures and the Information Science and Technology capability pillars and the Global Security mission area.
Reference
“An updated nuclear-physics and multi-messenger astrophysics framework for binary neutron star mergers,” Nature Communications, 14 (2023); DOI: 10.1038/s41467-023-43932-6. Authors: Peter T. H. Pang, Chris Van Den Broeck (Science Park 105); Tim Dietrich, Nina Kunert, Pouyan Salehi (Universität Potsdam); Michael W. Coughlin, Tyler Barna, Gargi Mansingh, Brandon Reed, Andrew Toivonen, Robert O. VandenBerg (University of Minnesota); Mattia Bulla (Stockholm University), Ingo Tews, Rahul Somasundaram (Los Alamos National Laboratory); Mouza Almualla (American University of Sharjah); Ramodgwendé Weizmann Kiendrebeogo (Université Joseph KI-ZERBO); Niharika Sravan (Drexel University); Sarah Antier (Université Côte d’Azur); Jack Heinzel (Massachusetts Institute of Technology); Vsevolod Nedora (Albert Einstein Institute); Ritwik Sharma (University of Delhi).
Technical contact: Ingo Tews (T-2)
Building hard physics constraints into deep convolutional neural networks for magnetohydrodynamics
The Los Alamos research team applied the physics-constrained neural network approach to magnetohydrodynamics for the study of the Orszag-Tang vortex, a widely used complex case for MHD simulation studies, showing that physics-constrained deep convolutional neural networks could be incorporated as solvers within physics models to speed up computationally expensive parts of the MHD simulation.
Magnetohydrodynamics, the study of the dynamics of electrically conducting fluids influenced by electromagnetic fields, describes a variety of systems important to many Los Alamos National Laboratory missions including manufacturing, materials science and particle accelerators such as LANSCE and DARHT. In a newly published paper featured on the cover of Physics of Plasmas, the Adaptive Machine Learning team from Los Alamos National Laboratory’s Applied Electrodynamics (AOT-AE) group developed a convolutional neural network-based method for simulating magnetohydrodynamics while respecting hard physics constraints. The team’s approach could greatly speed up computationally expensive MHD simulations.
MHD combines fluid dynamics with electrodynamics described by Maxwell’s equations. While many machine learning-based methods have been developed for surrogate models with the goal of speeding up physics models, a main limitation has been a lack of enforcement of hard physics constraints. The Adaptive Machine Learning team had previously developed a way to build hard physics constraints into physics-constrained 3D convolutional neural networks (PCNN) that respect Maxwell’s equations. In the newly published work, the Los Alamos team utilized that PCNN approach for application to magnetohydrodynamics for the study of the Orszag–Tang vortex, a widely used complex case for MHD simulation studies.
The Los Alamos team generated synthetic MHD data using physics simulations to test various physics-constrained neural network-based methods for creating surrogate models of the MHD simulation, which can quickly predict the evolution of the data, such as using magnetic field and charge density values at one time step to predict those fields at subsequent time steps. The team also showed that they could incorporating physics-constrained deep convolutional neural networks as solvers within physics models to speed up computationally expensive parts of the MHD simulation.
This work is a particular example of the Adaptive Machine Learning team’s work towards developing robust adaptive, physics-constrained deep learning-based generative models for Laboratory missions. Of particular relevance to Los Alamos’ mission, MHD describes phenomenon such as turbulence in plasmas, quantum plasmas, Tokamaks, plasma detachment in magnetic nozzles, liquid metals, and intense charged particle beams in particle accelerators. MHD simulations are complex, describing systems with many evolving nonlinear fields, which contributes to their computational expense.
Funding and mission
This work was supported by the Laboratory Directed Research and Development program at Los Alamos. The work supports the Global Security mission area and the Nuclear and Particle Futures capability pillar.
References
“Solving the Orszag–Tang vortex magnetohydrodynamics problem with physics-constrained convolutional neural networks,” Physics of Plasmas, 31 (2024). DOI: 10.1063/5.0172075. Authors: A. Bormanis, C. Leon, and A. Scheinker (Los Alamos National Laboratory).
“Physics-constrained 3D convolutional neural networks for electrodynamics,” APL Machine Learning, 1, 026109 (2023); DOI: 10.1063/5.0132433. Authors: Alexander Scheinker and Reeju Pokharel (Los Alamos National Laboratory).
Technical contact: Alexander Scheinker (AOT-AE)
Theoretical
Replication or death: What happens inside a virus-infected cell
Model for a constant viral release rate. Credit: The Royal Society under the terms of the Creative Commons Attribution License.
Reproduction number probability distributions using the model of viral release by budding. The reproduction number, R, is understood as a random variable representing the number of new cells infected by one initial infected cell in an otherwise susceptible target cell population. Credit: The Royal Society under the terms of the Creative Commons Attribution License.
When a virus infects a host cell, what determines if the infected cell will die or if the virus will take hold, spreading to other cells? Taking a mathematical approach to develop new stochastic models, Theoretical Division scientists at Los Alamos and their UK collaborators examined what can happen to a single infected cell during its lifetime.
As published in the Journal of the Royal Society Interface, their novel methods allow researchers to calculate the probability of viral extinction. The models suggest that an increase in the expected reproduction number (a random variable representing the number of new cells infected by one initial infected cell) does not automatically imply a decrease in the probability of extinction.
Relevant to the field of theoretical immunology and virology, these stochastic models of within-host viral dynamics can be used to calculate the probability that infection will become established in an individual cell, given an initial viral dose. In contrast, deterministic models, which don’t consider random variations, are limited to describing the mean behavior of the viral and infection dynamics, and thus, can only account for the average number of target cells infected by a single infected cell in a susceptible target cell population.
When a viral particle is taken up by a host cell, the genome is replicated and used to produce viral proteins. New viral particles are then assembled inside the infected cell. This team analyzed viral dynamics models with an eclipse phase: the period after a cell is infected but before it is capable of releasing virions. Their results show how to calculate the probability of viral extinction for different mechanisms of viral release: budding or bursting.
Funding and mission
The work was supported by the National Institutes of Health (NIH) and the Biotechnology and Biological Sciences Research Council (BBSRC). This work supports the Global Security mission area and the Complex Natural & Engineered Systems (CNES) capability pillar.
Reference
“The reproduction number and its probability distribution for stochastic viral dynamics,” Journal of the Royal Society Interface, 21, 20230400; DOI: 10.1098/rsif.2023.0400. Authors: Bevelynn Williams (University of Leeds, UK); Carmen Molina-París, Alan Perelson, Ruy Ribeiro (Los Alamos National Laboratory); Grant Lythe, Martín López-García (University of Leeds); Jonathan Carruthers (UK Health Security Agency); Joseph Gillard (Defense Science & Technology Lab, Salisbury, UK).
Technical contact: Carmen Molina-París (T-6)
