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At least 703 records · Page 39

Position Papers for the ASCR Workshop on Cybersecurity and Privacy for Scientific Computing Ecosystems

At the request of the Department of Energy's (DOE) Office of Advanced Scientific Computing Research (ASCR), this program committee has been tasked with organizing a workshop to identify basic research needs in cybersecurity and privacy to better support DOE's science and energy mission. As part of the process, the program committee is soliciting community input in the form of position papers to help identify significant use cases, facility issues, and other barriers to enabling verifiably trustworthy computational science while preserving data confidentiality as appropriate for scientific workflows of interest to DOE. The program committee will review these position papers and based on the fit of their area of expertise and interest, selected contributors will have the opportunity to participate in the workshop currently planned as a virtual event November 3-5th, 2021. The thrust areas that will be explored by this workshop are the following: (1) Algorithms for secure, scalable, privacy-enhancing technologies and frameworks, including: Federated AI/ML, Differential privacy, Randomized algorithms, Adversarial modeling & simulation, Graph algorithms, and Formal methods; (2) Platforms to support the entire scientific-computing ecosystem, including edge computing for large-scale experiments, focusing on heterogeneous systems and distributed systems, including: Heterogeneous computing systems, Distributed computing systems, and Secure data architectures; and (3) Data workflows to allow agile use of data while preserving integrity and privacy, making the important properties verifiable either at runtime or post-computation, including: Integrity and provenance and Data management infrastructure. Topics that are out-of-scope for the workshop include discussing specific proposed solutions or areas that are clearly out of DOE's fundamental and applied-sciences mission scope, e.g., cryptography, enterprise security, and general-operations technology.

97 MATHEMATICS AND COMPUTING↗

Experiences with SYCL on AMD GPUs with Kokkos

With the recent diversification of the hardware landscape in the high-performance computing (HPC) community, performance-portability solutions are becoming more and more important. One of the most popular choices is Kokkos, which recently became a Linux Foundation project. Most of its development is supported by the US Department of Energy and the French Alternative Energies and Atomic Energy Commission. Kokkos is implemented as a C++ library with multiple backends to support CPUs as well as various GPU architectures. These backends include OpenMP, CUDA, HIP, and also SCYL. This approach enables users to leverage the preferred vendor toolchain for the respective platform (e.g. CUDA, ROCm, OneAPI). The SYCL backend is used to target Intel GPUs, in particular to support the Aurora exascale supercomputer. However, SYCL itself also offers a large degree of portability, and in fact Kokkos’ CI for SYCL has been running on NVIDIA hardware due to a lack of access to Intel GPUs. In this report, we describe our experience with using Kokkos SYCL backend on AMD GPUs targeting the Frontier supercomputer at Oak Ridge National Laboratory. The two major SYCL implementations are DPC++ and AdaptiveCpp. While the Kokkos SYCL backend has been implemented using the former, the latter was the first implementation to target AMD GPUs. We will discuss the experience with both of these SYCL implementations in terms of functionality and performance. Using Kokkos to evaluate SYCL toolchains has a number of benefits. Kokkos’ use of SYCL is fairly complex, exercising features such as graphs, relocatable device functions, atomics – including for non-arithmetic types, as well as pinned and page migratable memory allocations. Kokkos also needs to implement capabilities such as Kokkos’ hierarchical parallelism that are not a straight-forward mapping to SYCL capabilities. Furthermore, a large number of libraries and applications that represent diverse use cases are implemented in Kokkos, providing readily available test cases for a toolchain evaluation. Preliminary results show that support for AMD GPUs in DPC++ is much less mature than for NVIDIA GPUs or Intel GPUs. While the situation has improved significantly over the last year, we still encounter many runtime failures, dispatching problems, and code generation issues. With AdaptiveCpp the challenges arise even earlier in the evaluation process. Since Kokkos’ SYCL implementation is largely focused on supporting Intel GPUs, we opted to leverage SYCL extensions which are available in DPC++ but not in AdaptiveCpp. Furthermore, AdaptiveCpp appears to be less conformant with the SYCL2020 standard which Kokkos relies on. In some cases, we are able to work around the lack of feature support, in other cases we have to disable certain Kokkos capabilities to evaluate the toolchain. Our evaluation will leverage Kokkos’ unit tests to establish basic functionality and feature completeness. We then use simple benchmarks for components of a CG implementation as a measure of usability and performance of the SYCL toolchains.

97 MATHEMATICS AND COMPUTING↗

Self-Evolving Machine: A Continuously Improving Model for Molecular Thermochemistry

Because collecting precise and accurate chemistry data is often challenging, chemistry data sets usually only span a small region of chemical space, which limits the performance and the scope of applicability of data-driven models. To address this issue, we integrated an active learning machine with automatic ab initio calculations to form a self-evolving model that can continuously adapt to new species appointed by the users. In the present work, we demonstrate the self-evolving concept by modeling the formation enthalpies of stable closed-shell polycyclic species calculated at the B3LYP/6-31G(2df,p) level of theory. By combining a molecular graph convolutional neural network with a dropout training strategy, the model we developed can predict density functional theory (DFT) enthalpies for a broad range of polycyclic species and assess the quality of each predicted value. For the species which the current model is uncertain about, the automatic ab initio calculations provide additional training data to improve the performance of the model. For a test set composed of 2858 cyclic and polycyclic hydrocarbons and oxygenates, the enthalpies predicted by the model agree with the reference DFT values with a root-mean-square error of 2.62 kcal/mol. Finally, we found that a model originally trained on hydrocarbons and oxygenates can broaden its prediction coverage to nitrogen-containing species via an active learning process, suggesting that the continuous learning strategy is not only able to improve the model accuracy but is also capable of expanding the predictive capacity of a model to unseen species domains.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A Magnetoencephalographic/Encephalographic (MEG/EEG) Brain-Computer Interface Driver for Interactive iOS Mobile Videogame Applications Utilizing the Hadoop Ecosystem, MongoDB, and Cassandra NoSQL Databases

In Phase I, we collected data on five subjects yielding over 90% positive performance in Magnetoencephalographic (MEG) mid-and post-movement activity. In addition, a driver was developed that substituted the actions of the Brain Computer Interface (BCI) as mouse button presses for real-time use in visual simulations. The process was interfaced to a flight visualization demonstration utilizing left or right brainwave thought movement, the user experiences, the aircraft turning in the chosen direction, or on iOS Mobile Warfighter Videogame application. The BCI’s data analytics of a subject’s MEG brain waves and flight visualization performance videogame analytics were stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse. In Phase II portion of the project involves the Emotiv Encephalographic (EEG) Wireless Brain–Computer interfaces (BCIs) allow for people to establish a novel communication channel between the human brain and a machine, in this case, an iOS Mobile Application(s). The EEG BCI utilizes advanced and novel machine learning algorithms, as well as the Spark Directed Acyclic Graph (DAG), Cassandra NoSQL database environment, and also the competitor NoSQL MongoDB database for housing BCI analytics of subject’s response and users’ intent illustrated for both MEG/EEG brainwave signal acquisition. The wireless EEG signals that were acquired from the OpenVibe and the Emotiv EPOC headset can be connected via Bluetooth to an iPhone utilizing a thin Client architecture. The use of NoSQL databases were chosen because of its schema-less architecture and Map Reduce computational paradigm algorithm for housing a user’s brain signals from each referencing sensor. Thus, in the near future, if multiple users are playing on an online network connection and an MEG/EEG sensor fails, or if the connection is lost from the smartphone and the webserver due to low battery power or failed data transmission, it will not nullify the NoSQL document-oriented (MongoDB) or column-oriented Cassandra databases. Additionally, NoSQL databases have fast querying and indexing methodologies, which are perfect for online game analytics and technology. In Phase II, we collected data on five MEG subjects, yielding over 90% positive performance on iOS Mobile Applications with Objective-C and C++, however on EEG signals utilized on three subjects with the Emotiv wireless headsets and (n < 10) subjects from the OpenVibe EEG database the Variational Bayesian Factor Analysis Algorithm (VBFA) yielded below 60% performance and we are currently pursuing extending the VBFA algorithm to work in the time-frequency domain referred to as VBFA-TF to enhance EEG performance in the near future. The novel usage of NoSQL databases, Cassandra and MongoDB, were the primary main enhancements of the BCI Phase II MEG/EEG brain signal data acquisition, queries, and rapid analytics, with MapReduce and Spark DAG demonstrating future implications for next generation biometric MEG/EEG NoSQL databases.

McClay, Wilbert↗

Augmented Topological Descriptors of Pore Networks for Material Science

One potential solution to reduce the concentration of carbon dioxide in the atmosphere is the geologic storage of captured CO 2 in underground rock formations, also known as carbon sequestration. There is ongoing research to guarantee that this process is both efficient and safe. We describe tools that provide measurements of media porosity, and permeability estimates, including visualization of pore structures. Existing standard algorithms make limited use of geometric information in calculating permeability of complex microstructures. This quantity is important for the analysis of biomineralization, a subsurface process that can affect physical properties of porous media. This paper introduces geometric and topological descriptors that enhance the estimation of material permeability. Our analysis framework includes the processing of experimental data, segmentation, and feature extraction and making novel use of multiscale topological analysis to quantify maximum flow through porous networks. We illustrate our results using synchrotron-based X-ray computed microtomography of glass beads during biomineralization. As a result, we also benchmark the proposed algorithms using simulated data sets modeling jammed packed bead beds of a monodispersive material.

36 MATERIALS SCIENCE↗

Initial Examination of Low Velocity Sphere Impact of Glass Ceramics

This report summarizes US Army TARDEC sponsored work at Oak Ridge National Laboratory (ORNL) involving low velocity (< 30 m/s or < 65 mph) sphere impact testing of two materials from the lithium aluminosilicate family reinforced with different amounts of ceramic particulate, i.e., glass-ceramic materials, SCHOTT Resistan™-G1 and SCHOTT Resistan™-L. Both materials are provided by SCHOTT Glass (Duryea, PA). This work is a follow-up to similar sphere impact studies completed by the authors on PPG's Starphire® soda-lime silicate glass and SCHOTT BOROFLOAT® borosilicate glass. A gas gun or a sphere-drop test setup was used to produce controlled velocity delivery of silicon nitride (Si 3 N 4 ) spheres against the glass ceramic tile targets. Minimum impact velocities to initiate fracture in the glass-ceramics were measured and interpreted in context to the kinetic energy of impact and the elastic property mismatch between sphere and target material. Quasistatic spherical indentation was also performed on both glass ceramics and their contact damage responses were compared to those of soda-lime silicate and borosilicate glasses. Lastly, variability of contact damage response was assessed by performing spherical indentation testing across the area of an entire glass ceramic tile. The primary observations from this low velocity (< 30 m/s or < 65 mph) testing were: (1) Resistan™-L glass ceramic required the highest velocity of sphere impact for damage to initiate. Starphire® soda-lime silicate glass was second best, then Resistan™-G1 glass ceramic, and then BOROFLOAT® borosilicate glass. (2) Glass-ceramic Resistan™-L also required the largest force to initiate ring crack from quasi-static indentation. That ranking was followed, in descending order, by Starphire® soda-lime silicate glass, Resistan™-G1 glass ceramic, and BOROFLOAT® borosilicate glass. (3) Spheres with a lower elastic modulus require less force to initiate fracture in Resistan™-G1 from quasi-static spherical indentation. This indicates that friction is affecting ring crack initiation in Resistan™-G1. Friction also affected ring crack initiation in Starphire® soda-lime silicate and BOROFLOAT® borosilicate glasses. Among these three materials, friction was the most pronounced (largest slope in the RCIF-elastic modulus graph) in the Starphire® and least pronounced in the BOROFLOAT®. The reason for this is not understood, but differences in deformation behavior under high contact stresses could be a cause or contributor to this. (4) The force necessary to initiate contact-induced fracture is higher under dynamic conditions than it is under quasi-static conditions in Resistan™-L and Resistan™-G1 glass ceramics. This is a trend observed too in Starphire® and BOROFLOAT®. (5) There is a subtle indication there was intra-tile differences in spherical indentation-induced ring crack initiation forces. This is not a material property nor is it exclusive to glass-ceramic Resistan™-G1 glass ceramic, rather, it is a statistical mechanical response to an accumulated history of processing and handling of that specific tile.

36 MATERIALS SCIENCE↗

Open-source generation of sigma profiles: impact of quantum chemistry and solvation treatment on machine learning performance

The combination of machine learning (ML) models with chemistry-related tasks requires the description of molecular structures in a machine-readable way. The nature of these so-called molecular descriptors has a direct and major impact on the performance of ML models and remains an open problem in the field. Structural descriptors like SMILES strings or molecular graphs lack size-independence and can be memory intensive. Machine-learned descriptors can be of low dimensionality and constant size but lack physical significance and human interpretability. Sigma profiles, which are unnormalized histograms of the surface charge distributions of solvated molecules, combine physical significance with low dimensionality and size-independence, making them a suitable candidate for a universal molecular descriptor. However, their widespread adoption in ML applications requires open access to sigma profile generation, which is currently not available. This work details the development of OpenSPGen – an open-source tool for generating sigma profiles. Also presented are studies on the effect of different settings on the efficacy of the generated sigma profiles at predicting thermophysical material properties when used as inputs to a Gaussian process as a simple surrogate ML model. We find that a higher level of theory does not translate to more accurate results. We also provide further recommendations for sigma profile calculation and use in ML models.

Salih, Fathya Y. M. [University of Notre Dame, IN ↗

A chemical pathway perspective on the kinetics of low-temperature ignition of propane

In this work, the chemistry of low-temperature ignition in propane/air mixtures is analyzed using a recently developed pathway representation of the chemical kinetics. The "Sum Over Histories Representation" allows time dependent kinetic observables to be computed using an expansion over global chemical pathways that follow chemical moieties as they move through a complex reaction network. This methodology assigns probabilities to complete chemical pathways through which specific intermediate or product species are generated. The growth of the radical pool during the ignition process is analyzed by enumerating chemical pathways that constitute catalytic cycles, in particular the catalyzed production of the highly reactive OH-radical. In addition to the well-known reaction route followed in low-temperature ignition of hydrocarbons which involves the QOOH and keto-hydroperoxide species, we have explicitly identified several other cycles that are responsible for most of the remaining OH-production.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Effect of realistic routing on the social burden metric

The distance people travel to reach critical services is a key input to the Social Burden metric used by Sandia’s Resilient Node Cluster Analysis Tool (ReNCAT) in the optimization’s objective function. By default, ReNCAT utilizes Euclidian distances between population blocks and critical facilities when calculating Social Burden. However, these straight-line distances do not reflect how most residents or goods would travel throughout the area. As distance is a vital input to the burden calculation, a more realistic distance calculation will yield more realistic burden values. This work uses real road networks and calculates the shortest distance path between population centers and critical facilities using a standard graph theory approach. These realistic route distances are then used to compute Social Burden for four areas of study. It was found that distances using real road routes are generally, but not always, longer than the Euclidean distance. The increased length increases the final Social Burden metric, however, the overall burden percent change ranged between 17% and 52%, which means the impact of realistic routes relies heavily upon the area’s road topology. It was found that rural locations within an area may have larger burden increases than urban areas as more dense road networks allow routes to more closely follow a straight-line path. Additionally, using the most straight forward routing algorithms requires high computational effort for areas with large road networks. While it is believed this process can be made more performant, that task is beyond this scope of work.

99 GENERAL AND MISCELLANEOUS↗

Predicting Small Molecule Transfer Free Energies by Combining Molecular Dynamics Simulations and Deep Learning

Accurately predicting small molecule partitioning and hydrophobicity is critical in the drug discovery process. There are many heterogeneous chemical environments within a cell and entire human body. For example, drugs must be able to cross the hydrophobic cellular membrane to reach their intracellular targets, and hydrophobicity is an important driving force for drug–protein binding. Atomistic molecular dynamics (MD) simulations are routinely used to calculate free energies of small molecules binding to proteins, crossing lipid membranes, and solvation but are computationally expensive. Machine learning (ML) and empirical methods are also used throughout drug discovery but rely on experimental data, limiting the domain of applicability. We present atomistic MD simulations calculating 15,000 small molecule free energies of transfer from water to cyclohexane. This large data set is used to train ML models that predict the free energies of transfer. We show that a spatial graph neural network model achieves the highest accuracy, followed closely by a 3D-convolutional neural network, and shallow learning based on the chemical fingerprint is significantly less accurate. A mean absolute error of ~4 kJ/mol compared to the MD calculations was achieved for our best ML model. We also show that including data from the MD simulation improves the predictions, tests the transferability of each model to a diverse set of molecules, and show multitask learning improves the predictions. This work provides insight into the hydrophobicity of small molecules and ML cheminformatics modeling, and our data set will be useful for designing and testing future ML cheminformatics methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

An artificial spiking quantum neuron

Abstract Artificial spiking neural networks have found applications in areas where the temporal nature of activation offers an advantage, such as time series prediction and signal processing. To improve their efficiency, spiking architectures often run on custom-designed neuromorphic hardware, but, despite their attractive properties, these implementations have been limited to digital systems. We describe an artificial quantum spiking neuron that relies on the dynamical evolution of two easy to implement Hamiltonians and subsequent local measurements. The architecture allows exploiting complex amplitudes and back-action from measurements to influence the input. This approach to learning protocols is advantageous in the case where the input and output of the system are both quantum states. We demonstrate this through the classification of Bell pairs which can be seen as a certification protocol. Stacking the introduced elementary building blocks into larger networks combines the spatiotemporal features of a spiking neural network with the non-local quantum correlations across the graph.

Kristensen, Lasse Bjørn (ORCID:0000000239398170)↗

EMPIRE-3.2 Malta: modular system for nuclear reaction calculations and nuclear data evaluation [User's Manual]

EMPIRE is a modular system of nuclear reaction codes, comprising various nuclear models, and designed for calculations over a broad range of energies and incident particles. The system can be used for theoretical investigations of nuclear reactions as well as for nuclear data evaluation work. Photons, nucleons, deuterons, tritons, helions (3He), α’s, and light or heavy ions can be selected as projectiles. The energy range starts just above the resonance region in the case of a neutron projectile, and extends up to few hundred MeV for heavy ion induced reactions. The code accounts for the major nuclear reaction models, such as optical model, Coupled Channels and DWBA (ECIS06 and OPTMAN), Multi-step Direct (ORION + TRISTAN), NVWY Multi-step Compound, exciton model (PCROSS), hybrid Monte Carlo simulation (DDHMS), and the full featured Hauser-Feshbach model including width fluctuations and the optical model for fission. Heavy ion fusion cross section can be calculated within the simplified coupled channels approach (CCFUS). A comprehensive library of input parameters based on the RIPL-3 library covers nuclear masses, optical model parameters, ground state deformations, discrete levels and decay schemes, level densities, fission barriers, and γ-ray strength functions. Effects of the dynamic deformation of a fast rotating nucleus can be taken into account in the calculations (BARFIT, MOMFIT). The results can be converted into the ENDF-6 format using the accompanying EMPEND code. Modules of the ENDF Utility Codes and the ENDF Pre-Processing codes are applied for ENDF file verification. The package contains the full EXFOR library of experimental data in computational format C4 that are automatically retrieved during the calculations. EMPIRE contains the resonance module that retrieves data from the electronic version of the Atlas of Neutron Resonances by Mughabghab (not provided with the EMPIRE distribution), to produce resonance section and related covariances for the ENDF-6 formatted files. EMPIRE can be used to determine covariances of the calculated data using either sensitivity matrices along with the KALMAN code or employing Monte Carlo approach to produce model generated covariances. In both cases experimental data can be taken into account, either directly (KALMAN) or by feeding the EMPIRE calculated Monte Carlo modelling covariance as a prior to the least square fitting GANDR system. Publication quality graphs can be obtained using the powerful and flexible plotting package ZVView. Interactive plots with ZVView comparing experimental results with calculations can be produced with ENDVER modules. The backbone of the EMPIRE system are bash-shell UNIX scripts that provide for seamless console operation of EMPIRE on Linux, Mac OS X, and Microsoft Windows with GNU gfortran compiler installed. Additionally, the graphical interface, provides for an easy operation of the system on Linux, Mac OS X and virtual Linux machines running on Microsoft Windows.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Community Detection on the GPU

We present and evaluate a new GPU algorithm based on the Louvain method for community detection. Our algorithm is the first for this problem that parallelizes the access to individual edges. In this way we can fine tune the load balance when processing networks with nodes of highly varying degrees. This is achieved by scaling the number of threads assigned to each node according to its degree. Extensive experiments show that we obtain speedups up to a factor of 270 compared to the sequential algorithm. The algorithm consistently outperforms other recent shared memory implementations and is only one order of magnitude slower than the current fastest parallel Louvain method running on a Blue Gene/Q supercomputer using more than 500K threads.

community detection↗

Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)

Ontologies are fundamental components of informatics infrastructure in domains such as biomedical, environmental, and food sciences, representing consensus knowledge in an accurate and computable form. However, their construction and maintenance demand substantial resources and necessitate substantial collaboration between domain experts, curators, and ontology experts. We present Dynamic Retrieval Augmented Generation of Ontologies using AI (DRAGON-AI), an ontology generation method employing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). DRAGON-AI can generate textual and logical ontology components, drawing from existing knowledge in multiple ontologies and unstructured text sources.We assessed performance of DRAGON-AI on de novo term construction across ten diverse ontologies, making use of extensive manual evaluation of results. Our method has high precision for relationship generation, but has slightly lower precision than from logic-based reasoning. Our method is also able to generate definitions deemed acceptable by expert evaluators, but these scored worse than human-authored definitions. Notably, evaluators with the highest level of confidence in a domain were better able to discern flaws in AI-generated definitions. We also demonstrated the ability of DRAGON-AI to incorporate natural language instructions in the form of GitHub issues.These findings suggest DRAGON-AI's potential to substantially aid the manual ontology construction process. However, our results also underscore the importance of having expert curators and ontology editors drive the ontology generation process.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Thermal and Physiochemical Characterization of Lignin Extracted from Wheat Straw by Organosolv Process

The purified lignin extracted from wheat straw by organosolv process was characterized for the existence of lignin subunits, functional groups and physical and thermal behaviours. Presence of carboxyl, hydroxyl, methoxyl groups and C–C, C–O, C=O linkages were conducted by Fourier Transform Infrared (FT-IR). By {sup 1}H-NMR (Nuclear Magnetic Resonance Spectroscopy) presence of aromatic protons, phenolic hydroxyls, carboxylic acids, and aldehydes were established. Moreover, {sup 13}C-NMR spectra have disclosed the presence of condensed and uncondensed aliphatic and aromatic aryls and ethers. The existence of syringyl and guaiacyl units also were confirmed with both FTIR and NMR spectroscopies. Furthermore, the results from Thermal Gravimetric Analysis (TGA) showed the weight of extracted lignin is changed in temperatures between 180 and 670 °C by devolatization, formation and condensation of different chemicals. Also, the Differential Scanning Calorimetry (DSC) graph displayed a glass transition temperature of 105 °C for extracted lignin. As a result, the extracted lignin with high purity can be a suitable candidate for carrying out value-added products.

36 MATERIALS SCIENCE↗

Illuminating the pathways to carbon liberation: a systems approach to characterizing the consequential unknowns of carbon transformation and loss from thawing permafrost peatlands (Final Report)

The IsoGenie3 Project delivered new systems-level insights into carbon cycling in thawing permafrost landscapes, with an emphasis on methane and carbon dioxide emissions. From >200 samples from the site collected over a decade, co-analyzed for geochemistry and microbiology, the team recovered ~1,500 assembled microbial genomes and ~1,900 viral population genomes, revealing appreciable genetic novelty - from a new highly abundant bacterial phylum, to novel methane consumers and their activities, to rampant viral novelty. IsoGenie3 linked these organisms to carbon compound transformations (which define the cycling of organic matter in soils, and the loss of the greenhouse gases carbon dioxide and methane), and saw that the microbes at each stage of permafrost thaw had different genetic potential to degrade categories of compounds, expressed that genetic potential differently, and actually transformed carbon compounds into greenhouse gases in different ways. IsoGenie 3 identified that some of the thaw-stage differences were due to plant-microbiome relationships; the plant species across the thaw gradient contributed different carbon compounds into the soil, and hosted distinct microbiota (differing among parts of plants as well as species). Lastly, microbes in the saturated post-thaw conditions appeared likely to contribute to the mobilization and toxification of mercury released during thaw. In parallel with ongoing field sampling and analysis, hypotheses arising from field observations were tested via lab incubation experiments. When communities are taken out of their native habitats, they behave differently, and the team first rigorously quantified the magnitude of this effect on microbiome composition and functional capacity, organic matter composition, and gas production; overall the main system processes were maintained in the lab incubations under the conditions tested. Further, the microbial data could inform geochemical reaction network models of those processes. Then, the team ran experiments with additions of compounds, varying temperature, and “live” vs. “dead” peat (the latter having been gamma irradiated, with a few additional variants to control for methodological artifacts). From these, we (a) determined the importance of plant-derived soluble phenolic compounds in bogs’ extraordinary recalcitrance of organic matter, and carbon gas emissions skewed to carbon dioxide; (b) proposed an abiotic ‘tanning’ mechanism, which could contribute to Sphagnum’s inhibitory effect on anaerobic decomposition through alteration of N availability. IsoGenie3 illuminated longer-term and landscape-scale interactions of permafrost thaw and carbon cycling, advancing knowledge of the drivers of methane dynamics not only across in the permafrost-associated peatland (where hydrology and plant communities dictate microbiomes) but also their interconnected lakes (where sediment carbon quality and resident microbiota are determined by position within lake, and lake features). By leveraging observations of site methane dynamics extending well before this project, the team was able to construct a 44-year portrait of the interplay of permafrost thaw, hydrology, vegetation dynamics, and carbon gas emissions, and the doubling of the fully-thawed fens over this time. From the detailed study of this focal site, IsoGenie3 also aimed to improve model representation of these kinds of sites and processes. To improve predictions of methane transformations, we incorporated acetate and isotope dynamics into the ‘DNDC’ biogeochemistry model. In addition, recovered genomes were grouped into ‘functional groups’, i.e. the genomes that perform a specific function of interest, then used to parameterize maximum growth rate and optimum growth temperature (via signatures in their sequence composition) for the BioCrunch model. The BioCrunch model was then in turn used to test the impact of increasing functional resolution of the microbes, on the carbon gas emissions. Lastly for modeling, the ecosys model was parameterized from the microbial and other data, and used to evaluate drivers of e.g. change in methane emissions. Finally, this project also led to the development of a range of new methods and tools, a new metric of organic matter decomposability, as well as a graph-database solution to multidisciplinary data storage and querying. This project’s ongoing analyses at our focal site also contributed to broader advancements in understanding elements of genetic plasticity and methane metabolism, climate change microbiology and community assembly, global peatland geochemistry and Arctic lakes’ roles in climate feedbacks.

54 ENVIRONMENTAL SCIENCES↗

Data Validation Package May 2016 Groundwater Sampling at the Lakeview, Oregon, Processing Site August 2016

This biennial event includes sampling five groundwater locations (four monitoring wells and one domestic well) at the Lakeview, Oregon, Processing Site. For this event, the domestic well (location 0543) could not be sampled because no one was in residence during the sampling event (Note: notification was provided to the resident prior to the event). Per Appendix A of the Groundwater Compliance Action Plan, sampling is conducted to monitor groundwater quality on a voluntary basis. Sampling and analyses were conducted as specified in the Sampling and Analysis Plan for U.S. Department of Energy Office of Legacy Management Sites (LMS/PRO/S04351, continually updated). One duplicate sample was collected from location 0505. Water levels were measured at each sampled monitoring well. The constituents monitored at the Lakeview site are manganese and sulfate. Monitoring locations that exceeded the U.S. Environmental Protection Agency (EPA) Secondary Maximum Contaminant Levels for these constituents are listed in Table 1. Review of time-concentration graphs included in this report indicate that manganese and sulfate concentrations are consistent with historical measurements.

2016 Groundwater Sampling Lakeview, Oregon↗