News & Media
Secretary of Energy Advisory Board (SEAB) Report Recognizes PNNL Contributions
Report features how PNNL’s computing capabilities are affecting the nation’s security, science, and energy missions
Contributions from researchers across Pacific Northwest National Laboratory (PNNL) were recognized in the preliminary findings of a Secretary of Energy Advisory Board (SEAB) report from a working group dedicated to the U.S. Department of Energy’s (DOE’s) capabilities and future in artificial intelligence (AI) and machine learning. PNNL researchers’ expertise is prominent throughout DOE’s AI efforts, particularly in the areas of data sciences and national security.
Based largely on input from DOE sponsors, the report features how PNNL’s computing capabilities are affecting the nation’s security, science, and energy missions. Key highlights include:
- Studying how AI affects the global landscape for securing nuclear materials, potentially using deep learning to enhance physical and digital protections against material concealment, delivery, theft, and sabotage.
- Describing how the United States and its partners might employ deep learning to combat attack efforts for enhanced nuclear security.
- Designing advanced deep learning models to characterize operations with buildings, using electrical signatures on power lines, enabling new designs for energy-efficient buildings in addition to enhanced security features for nuclear facilities.
- Leading the nuclear explosive monitoring project with data scientists working to significantly lower detection thresholds of low-yield, evasive underground nuclear explosions without increasing time-to-detection or the amount of human analysis.
- Co-design of advanced accelerator, memory and data movement concepts to support convergence of AI and machine learning methods with other forms of data analytics and traditional scientific high performance computing (HPC).
The report highlights PNNL’s support to the National Nuclear Security Administration, featuring joint laboratory collaborations between PNNL and others, including the Y-12 National Security Complex, Sandia National Laboratories, Lawrence Livermore National Laboratory, Los Alamos National Laboratory, and Oak Ridge National Laboratory. Additionally, PNNL is working as part of DOE’s comparative advantages in AI, providing the Office of Energy Efficiency and Renewable Energy access to AI subject matter experts.
Study Shows Coastal Wetlands Aid in Carbon Sequestration
Sea-level rise impacts will likely decrease ecosystem carbon stocks
Solving a MOF Mystery
Revealing the kinetic controls of metal-organic framework/metal oxide heterostructure
Metal-organic frameworks (MOFs)/semiconducting oxide heterostructures exhibit unique properties beyond those of individual components, but their design requires an understanding of energetic and kinetic controls at MOFs–substrate interfaces. Although structure relationship has been widely applied in heterostructure design, it overlooks the interplay between organic ligands, or linkers, and substrate, which defines the kinetics and energetics in the final structure. Herein, we used zeolitic imidazolate frameworks (ZIF-8) on ZnO as a model system to evaluate this interplay via in situ monitoring and simulations.
This is the first piece of systematic research on unraveling the physical controls of linkers on metal oxide surface step kinetics by in situ atomic force microscopy monitoring combined with ab initio molecular dynamics (AIMD)—a realistic simulation of complex systems—and density-function theory (DFT), which control the dissolution kinetics of substrate, and therefore the overgrowth kinetics of MOFs/metal oxide heterostructural materials.
This research addresses two topics that are not well understood in literature: the interplay between organic linkers and substrates during MOF crystallization, as well as the mechanisms that control heterostructure formation in solutions. It is the first piece of systematic research on unraveling the physical controls of organic linkers on metal oxide step kinetics, which controls the overgrowth rate of overlayer MOF materials by in situ monitoring combined with ab initio molecular dynamics and DFT theory. The fact that the linker 2-methyl-imidazole (2-MIM) can simultaneously act by three different mechanisms is fascinating (“dissolution-promoter,” “step-pinner,” and “terrace-binder”), and is also a unique concept given that all three are observed on a single surface—the ZnO (001) face. In contrast, only “dissolution-promoter” and “terrace-binder” are identified for the ZnO (100) face—this is a novel observation. These differences define the ZnO-face-specific growth kinetics of MOF/ZnO heterostructures. In principle, our mechanism should have a wide implication for other biological, energy, and environmental heterostructural materials formation, especially in solution conditions.
Materials Scientist, Pacific Northwest National Laboratory
James De Yoreo
Battelle Fellow, Pacific Northwest National Laboratory
Battelle Fellow, Pacific Northwest National Laboratory
This research was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Division of Materials Sciences and Engineering, Synthesis and Processing Sciences Program. XRD and SEM characterization were performed in the Environmental Molecular Sciences Laboratory (EMSL), a national scientific user facility sponsored by the Department of Energy's Office of Science Biological and Environmental Research program located at Pacific Northwest National Laboratory (PNNL). Computational resources were provided by PNNL’s Research Computing and EMSL user facility.
J. Tao et al, “Controlling Metal-Organic Framework/ZnO heterostructure kinetics through selective ligand binding to ZnO surface steps.” Chemistry of Materials, (2020) http://doi.org/10.1021/acs.chemmater.0c02123
Deconstructing the Soil Microbiome
Deconstruction of soil microbial communities into discrete functional groups enables piecing together the functional potential of the complex soil microbiome
The soil microbiome plays a major role in nutrient cycling and plant health. However, its inherent complexity, with a vast array of microbes that metabolize many different molecules, makes it challenging to effectively analyze ecosystem functions performed by interacting members of soil microbial communities. Researchers dissected the complex microbial community of a native Washington soil into reproducible, low-complexity communities called 'functional modules.' Because these subcommunities are easier to study than a bulk community, researchers could analyze microbial species and functions present in the soil in more depth than before.
By studying discrete functional components of the soil microbiome at high resolution, the researchers obtained a more complete picture of soil diversity compared to analysis of the entire soil community. They identified specific evolutionary relationships and biochemical characteristics of the soil microbiome that otherwise would have been hidden in previous community-scale genomic analyses. Improved understanding of the functions of the soil microbiome could help scientists harness beneficial aspects of the soil microbiome to increase soil health or crop productivity.
One gram of soil contains microbes from thousands of different evolutionary groups. These microbes also have a wide variety of metabolic functions that help them survive in different soil microenvironments. Analyzing the complete functional and taxonomic diversity of a soil microbiome requires a large amount of computing power, and it may fail to capture large populations of quiet or rare microbes.
To simplify the analysis of a soil microbial community, researchers incubated a parent soil microbiome under several different conditions to create different subcommunities of microbes with specific functions, or functional modules. The functional modules included: usage of simple and complex carbon substrates, antibiotic resistance, anaerobic growth with different redox acceptors, and stress resistance. For each functional module, the researchers performed 16S rRNA gene amplicon sequencing to determine the community composition and RNA sequencing to identify expressed functions. Approximately 27% of unique taxa present in the parent soil were found in the functional modules, in addition to 341 taxa not detected in the parent community. The functional modules had unique gene expression patterns that were also enriched for transcripts associated with functional characteristic of each module. By dissecting the soil microbiome into discrete components, the researchers obtained a more comprehensive and highly detailed view of a soil microbiome and its biochemical potential than through analysis of a soil microbiome as a whole.
Ryan McClure, Pacific Northwest National Laboratory, firstname.lastname@example.org
This research was supported by the U.S. Department of Energy’s Office of Science, Biological and Environmental Research Program and is a contribution of the Scientific Focus Area “Phenotypic response of the soil microbiome to environmental perturbations.”
D. Naylor, et al., “Deconstructing the Soil Microbiome into Reduced-Complexity Functional Modules.” mBio 11, e01349-20 (2020). [DOI: 10.1128/mBio.01349-20]