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DOE OSTI2021

Deep-freeze graph training for latent learning

Scientific and engineering advances are primarily driven by multi-tier conceptual constructs and conditional theoretical frameworks. The theories allow predictions of hypothetical system responses, given a set of approximate conditions (ranges of applicability) imposed on latent parameters that cannot be measured directly. Learning to estimate the latent variables (Latent Learning) helps to pinpoint the anticipated range-edge anomalies and improves the confidence in interpretation, interpolation and extrapolation of limited experimental data. Due to high dimensionality and extreme non-linearity of the materials science problems, very large datasets are typically required for conventional data-driven model development. The vital experimental data collection, particularly on microstructural phases, is very challenging, which makes it difficult to compile a high-quality database. Incorporation of the domain knowledge into the computational graph structure, initialization and optimization processes presents a viable mechanism for developing accurate models, with limited datasets. Furthermore, this study successfully utilized the approach to build the Deep Freeze Graph (DeepFreG) by mapping known causality relationships and by digitizing empirical domain knowledge for Latent Learning (LL), with specific applications in materials science.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Overcoming small minirhizotron datasets using transfer learning

Minirhizotron technology is widely used to study root growth and development. Yet, standard approaches for tracing roots in minirhiztron imagery is extremely tedious and time consuming. Machine learning approaches can help to automate this task. However, lack of enough annotated training data is a major limitation for the application of machine learning methods. Transfer learning is a useful technique to help with training when available datasets are limited. In this paper, we investigated the effect of pre-trained features from the massives-cale, irrelevant ImageNet dataset and a relatively moderate-scale, but relevant peanut root dataset on switchgrass root imagery segmentation applications. We compiled two minirhizotron image datasets to accomplish this study: one with 17,550 peanut root images and another with 28 switchgrass root images. Both datasets were paired with manually labeled ground truth masks. Deep neural networks based on the U-net architecture were used with different pre-trained features as initialization for automated, precise pixel-wise root segmentation in minirhizotron imagery. We observed that features pre-trained on a closely related but relatively moderate size dataset like our peanut dataset were more effective than features pre-trained on the large but unrelated ImageNet dataset. Here, we achieved high quality segmentation on peanut root dataset with 99.04% accuracy at the pixel-level and overcame errors in human-labeled ground truth masks. By applying transfer learning technique on limited switchgrass dataset with features pre-trained on peanut dataset, we obtained 99% segmentation accuracy in switchgrass imagery using only 21 images for training (fine tuning). Furthermore, the peanut pre-trained features can help the model converge faster and have much more stable performance.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2012

Implementing Molecular Dynamics on Hybrid High Performance Computers - Particle-Particle Particle-Mesh

The use of accelerators such as graphics processing units (GPUs) has become popular in scientific computing applications due to their low cost, impressive floating-point capabilities, high memory bandwidth, and low electrical power requirements. Hybrid high-performance computers, machines with nodes containing more than one type of floating-point processor (e.g. CPU and GPU), are now becoming more prevalent due to these advantages. In this paper, we present a continuation of previous work implementing algorithms for using accelerators into the LAMMPS molecular dynamics software for distributed memory parallel hybrid machines. In our previous work, we focused on acceleration for short-range models with an approach intended to harness the processing power of both the accelerator and (multi-core) CPUs. To augment the existing implementations, we present an efficient implementation of long-range electrostatic force calculation for molecular dynamics. Specifically, we present an implementation of the particle-particle particle-mesh method based on the work by Harvey and De Fabritiis. We present benchmark results on the Keeneland InfiniBand GPU cluster. We provide a performance comparison of the same kernels compiled with both CUDA and OpenCL. We discuss limitations to parallel efficiency and future directions for improving performance on hybrid or heterogeneous computers.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

The VENUS/NWChem Software Package. Tight Coupling Between Chemical Dynamics Simulations and Electronic Structure Theory

The interface for VENUS and NWChem, and the resulting software package for direct dynamics simulations are described. The coupling of the two codes is considered to be a tight coupling. The two codes are compiled and linked together and act as one executable with data being passed between the two codes through routine calls. The advantages of this type of coupling are discussed. The interface has been designed to have as little interference as possible with the core codes of both VENUS and NWChem. VENUS is the code that propagates the direct dynamics trajectories and, therefore, is the program that drives the overall execution of VENUS/NWChem. VENUS has remained an essentially sequential code, which uses the highly parallel structure of NWChem. Subroutines of the interface which accomplish the data transmission and communication between the two computer programs are described. Recent examples of the use of VENUS/NWChem for direct dynamics simulations are summarized.

chemical dynamics simulation↗

DOE OSTI2022

tapir: A tool for topologies, amplitudes, partial fraction decomposition and input for reductions

The demand for precision predictions in the field of high energy physics has dramatically increased over recent years. Experiments conducted at the LHC, as well as precision measurements at the intensity frontier such as Belle II require equally precise theoretical predictions to make full use of the acquired data. To match the experimental precision, second-, third- and, for certain quantities, even higher-order calculations in perturbative quantum field theory are required. To facilitate such calculations, computer software automating as many steps as possible is required. Yet, each calculation poses different challenges and thus, a high level of configurability is required. In this context we present tapir: a tool for identification, manipulation and minimization of Feynman integral families. It is designed to integrate in toolchains based on the computer algebra system FORM, the use of which is common practice in the field. tapir can be used to reduce the complexity of multi-loop problems with cut-filters, topology mapping, partial fraction decomposition and alike. Program Title:tapir CPC Library link to program files:https://doi.org/10.17632/ptc9t46xyn.1 Developer's repository link:https://gitlab.com/tapir-devs/tapir Licensing provisions: GPLv3 Programming language:python 3, C++ Nature of problem: Multi-loop computations require the automatization of a large number of different tasks related to Feynman integral topologies. Among them are the identification and minimization of integral topologies, partial fraction decomposition of topologies in the case of linearly dependent propagators as well as mapping scalar products of loop momenta to scalar functions. Solution method: The minimization of topologies is performed by comparison of their respective Nickel indices [1], even further minimization utilizes Pak's algorithm [2]. To efficiently map scalar products of loop momenta to scalar functions FORM [3] code is generated. Additional comments including restrictions and unusual features: Minimization based on Pak's algorithm slows down for many lines and scales. A coarser minimization using the Nickel indices, however, is still possible. [1]B. Nickel, D. Meiron, G.A.J. Baker, Compilation of 2-pt and 4-pt graphs for continuous spin model, Report, University of Guelph, 1977.[2]A. Pak, J. Phys. Conf. Ser. 368 (2012) 012049, https://doi.org/10.1088/1742-6596/368/1/012049, arXiv:1111.0868.[3]B. Ruijl, T. Ueda, J. Vermaseren, FORM version 4.2, arXiv:1707.06453, 7 2017.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Thermo4PFM: Facilitating Phase-field simulations of alloys with thermodynamic driving forces

Phase-field modeling is a popular front-tracking approach used to model solidification. Its time-evolution equations are often coupled to alloy composition and/or thermal diffusion in high-resolution multiphysics approaches. Materials thermodynamic properties tabulated in CALPHAD databases can be used for phase-field modeling to parameterize bulk energies of alloys. In addition, they can be naturally integrated into models such as the Kim-Kim-Suzuki (KKS) model where driving forces depend on the differences between chemical potentials of co-existing phases. In that case, a small system of coupled nonlinear equations needs to be solved at every point in space where the phase-field order parameter is to be updated and evolved in time. Here we present Thermo4PFM, a solver for the KKS equations for binary and ternary alloys, with two or three phases, and parameterized with CALPHAD models. Thermo4PFM is open source, written in C++, and can take advantage of Graphics Processing Units (GPU) accelerators. Using OpenMP offload capabilities for C++ classes, an excellent performance is demonstrated on GPU using the LLVM compiler. CALPHAD data is read from simple JSON files using an open source parser from the boost library.

36 MATERIALS SCIENCE↗

DOE OSTI2025

JuTrack: A Julia package for auto-differentiable accelerator modeling and particle tracking

Efficient accelerator modeling and particle tracking are key for the design and configuration of modern particle accelerators. In this work, we present JuTrack, a nested accelerator modeling package developed in the Julia programming language and enhanced with compiler-level automatic differentiation (AD). With the aid of AD, JuTrack enables rapid derivative calculations in accelerator modeling, facilitating sensitivity analyses and optimization tasks. Here we demonstrate the effectiveness of AD-derived derivatives through several practical applications, including sensitivity analysis of space-charge-induced emittance growth, nonlinear beam dynamics analysis for a synchrotron light source, and lattice parameter tuning of the future Electron-Ion Collider (EIC). Through the incorporation of automatic differentiation, this package opens up new possibilities for accelerator physicists in beam physics studies and accelerator design optimization.

43 PARTICLE ACCELERATORS↗

DOE OSTI2018

Regenerator design optimization: Results from REGEN 3.3

The results from an extensive parametric study using the most recent version of the regenerator design software tool, REGEN3.3, are presented. By compiling the results from more than 10,000 applications of REGEN3.3 for three different temperatures (35 K, 60 K and 80 K), a wide range of frequencies (30–300 Hz), and a variety of regenerator length, inverse mass flux, and cold end phase angles, a group of performance maps have been generated that will enable regenerator optimization without having to learn REGEN3.3. Optimum results, defined by maximum COP values, are also provided in the form of a convenient correlation whereby the COP can be calculated directly from chosen values of cold end temperature, frequency, inverse mass flux, length, and cold end phase angle. Here one of the more interesting results revealed by this study is that the optimum value of COP is very broad. That is, changes by as much as 15% to 20% in the length, inverse mass flux, or cold end phase away from their optimized values degrade the COP by only 2%.

42 ENGINEERING↗

DOE OSTI2020

A dataset of eco-evidence tools to inform early-stage environmental impact assessments of hydropower development

The datasets described herein provide the foundation for a decision support prototype (DSP) toolkit aimed at assisting stakeholders in determining evidence of which aspects of river ecosystems have been impacted by hydropower. The DSP toolkit and its application are presented and described in the article “Evidence-based indicator approach to guide preliminary environmental impact assessments of hydropower development” [1]. Development of the DSP and the output for decision support centralize around 42 river function indicators describing the dimensionality of river ecosystems through six main categories: biota and biodiversity, water quality, hydrology, geomorphology, land cover, and river connectivity. Three main tools are represented in the DSP: A science-based questionnaire (SBQ), an environmental envelope model (EEM), and a river function linkage assessment tool (RFLAT). The SBQ is a structured survey-style questionnaire whose objective is to provide evidence of which indicators have been impacted by hydropower. Based on a global literature review, 140 questions were developed from general hypotheses regarding the impacts of dams on rivers. The EEM is a model to predict the likelihood of hydropower impacting indicators based on a several variables. The intended use of the EEM is for situations of new hydropower development where results of the SBQ are incomplete or highly uncertain. The EEM was developed through the compilation of a dataset containing attributes of dams, reservoirs, and geospatial information on environmental concerns, which was combined with data on ecological indicators documented at those sites through literature review. The model operates through 247 “envelopes” and weighting factors, representing the individual effect of each variable on each indicator, all available through spreadsheets. Finally, the RFLAT is a tool to examine causal relationships amongst indicators. Inter-indicator relationships were hypothesized based on literature review and summarized into node and edge datasets to represent the structure of a graphical network. Bayes theorem was used estimate conditional probabilities of inter-indicator relationships based on the output of the SBQ. Nodes and edges were imported into R programming environment to visualize ecological indicator networks. The datasets can be expanded upon and enriched with more detailed questions for the SBQ, building upon the EEM with to develop more sophisticated models, and identifying new relationships for the RFALT. Additionally, once the tools are applied to numerous hydropower developments, the output of the tools (e.g. evidence of impacted indicators) becomes a very useful dataset for meta-analyses of hydropower impacts.

13 HYDRO ENERGY↗

DOE OSTI2021

A critical review of molybdenum sequestration mechanisms under euxinic conditions: Implications for the precision of molybdenum paleoredox proxies

The observed difference in molybdenum (Mo) mobility and isotopic fractionation under oxic versus euxinic (i.e., anoxic and sulfidic) aqueous conditions provides a sound operational basis for the use of Mo geochemical signatures in ancient sedimentary records to infer palaeoceanographic redox conditions. While Mo is known to exist predominantly as molybdate (MoO 4 2– ) in oxic waters and convert into thiomolybdate species (MoO x S 4-x 2– ) under euxinic conditions, the pathways that lead to Mo sequestration are highly debated. As mechanistic understanding of Mo sequestration is crucial for accurately reconstructing the chemistry of ancient oceans and constraining the timing and intensity of oxygenation events through Earth's history using Mo paleoproxies, we have closely examined the current proposed mechanisms for Mo sequestration across a wide range of euxinic conditions. Through compilation and comparison of such information, we aim to provide an integrated view of Mo sequestration processes, to identify the current controversies as well as the roots of such controversies, and importantly, to propose avenues for future research. Here, sequestration of Mo may occur through complexation with organic matter (OM), reactions with oxyhydroxide and sulfide mineral surfaces, incorporation into iron sulfide crystal structures, formation of Mo-sulfide, and cellular assimilation. However, several major questions remains unresolved, including whether OM complexation plays a significant role in overall sedimentary Mo accumulation, whether the reduction of Mo(VI) occurs during interaction with Fe-S clusters/precipitates and/or with OM, whether microbial enzymatic reduction of Mo(VI) is important in Mo sequestration, whether synergistic effects exist between the biological and abiotic processes in Mo sequestration, and whether differences in reactivity among the various thiomolybdate intermediate species and tetrathiomolybdate influence the Mo sequestration process. At the end of the review, the reliability of Mo paleoproxies is discussed and reevaluated.

58 GEOSCIENCES↗

DOE OSTI2014

Impact of induced seismic events on seal integrity, Texas Gulf Coast

Recent publications have suggested that large-scale CO 2 injection could trigger earthquakes and that even small- to moderate-sized earthquakes may threaten the seal integrity of the injection zone, and potentially damage buildings and other surface structures. In this study, we compared seal thickness to estimated fault displacement due to a single hypothetical seismic event in a selected area of the Texas Gulf Coast comprising an offshore strip of state waters along two Texas counties. To evaluate the slip generated by a single seismic event, we compiled well log information on shale/sand sequences and seismic information on fault geometric characteristics of a section of Lower Miocene age. The section is thousands of feet thick and is overlain and underlain by marine shales (Amph. B and Anahuac, respectively) that are relatively easy to correlate between wells. The Amph. B. shale is the secondary and ultimate seal for all injection intervals in the Lower Miocene. Given its thickness, no realistic seismic event or small series of seismic events will offset it significantly. However, this may not be true of smaller local primary seals. An analysis of geophysical logs of a total of 71 wells yielded a total of 2,871 sand / shale binary intervals. An analysis of the dedicated 3D seismic survey counted 723 fault traces at five roughly horizontal horizons within the Lower Miocene Fault displacement estimated using the product of the fault length times an uncertain multiplier coefficient assumed to follow a triangular distribution with a 10 -3 to 10 -5 range and a mode of 8 × 10 -5 . We then compared estimated single-event fault displacements to seal thicknesses by means of a Monte-Carlo analysis. Only 1.8% of thickness/displacement pairs display a displacement greater than 20% of the seal thickness. Only 0.26% of the pairs result in a displacement of half the seal thickness and only 0.05% of thickness/displacement pairs result in a clear seal rupture. The next step was to compare the magnitude of the event generated by such a displacement to documented magnitudes of “large” earthquakes generated by waterflooding and fluid disposal. Based on this analysis, we conclude that seismicity that may arise from CO 2 injection appears not to be a serious complication for CO 2 storage integrity, at least in the Gulf Coast area.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Social cost impact assessment of pipeline infrastructure projects

A key advantage of trenchless construction methods compared with traditional open-cut methods is their ability to install or rehabilitate underground utility systems with limited disruption to the surrounding built and natural environments. The equivalent monetary values of these disruptions are commonly called social costs. Social costs are often ignored by engineers or project managers during project planning and design phases, partially because they cannot be calculated using standard estimating methods. In recent years some approaches for estimating social costs were presented. Nevertheless, the cost data needed for validation of these estimating methods is lacking. Development of such social cost databases can be accomplished by compiling relevant information reported in various case histories. This paper identifies eight most important social cost categories, presents mathematical methods for calculating them, and summarizes the social cost impacts for two pipeline construction projects. The case histories are analyzed in order to identify trends for the various social cost categories. The effectiveness of the methods used to estimate these values is also discussed. These findings are valuable for pipeline infrastructure engineers making renewal technology selection decisions by providing a more accurate process for the assessment of social costs and impacts. - Highlights: • Identified the eight most important social cost factors for pipeline construction • Presented mathematical methods for calculating those social cost factors • Summarized social cost impacts for two pipeline construction projects • Analyzed those projects to identify trends for the social cost factors.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Assessing direct and indirect emissions of greenhouse gases in road transportation, taking into account the role of uncertainty in the emissions inventory

Highlights: • The use-chain model implemented in this paper is effective in minimizing uncertainty. • Data integration is important to reduce uncertainty. • An innovative metric, Emission Value at Risk, was used to adjust uncertainty values. • Indirect impacts of economic activities change the estimate of GHG emissions. - Abstract: Greenhouse gas (GHG) concentration in the atmosphere has increased since the beginning of the industrial era, with dramatic effects on climate change. Transportation is one of the main sources of GHGs, with more than two-thirds of transport-related GHG emissions attributable to road vehicles. Any policy that aims to reduce GHG emissions needs robust measuring methods that guarantee the quality and reliability of primary data and estimates. However, these estimates are subject to uncertainty, both at the stage of compiling accounting tables and at the stage of using this information to formulate a specific policy question. This paper considers how to reduce uncertainty in estimating GHG emissions from road transportation, with specific reference to a regional emissions inventory in Italy. We propose the application of a use-chain model that can tackle uncertainty in measuring GHG emissions by enhancing the quality of the emissions data registry in the inventory. This new metric, which we call emission value at risk (VaR), draws from methodologies and concepts employed in the insurance and financial sectors. Moreover, additional assessments are performed, integrating the inventory data with those available in the regional energy balance and disaggregated sectoral economic dataset. The results show that a sound accounting method enables uncertainty in emission data to be taken into account, thus improving the design of appropriate strategies to reduce GHG emissions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Land use and turbine technology influences on wind potential in the United States

As clean energy ambitions have expanded, critically evaluating renewable energy supply has become increasingly important to the energy research community and stakeholders. This study examines the onshore wind resource potential for the conterminous United States and its sensitivity to siting constraints and turbine technology innovation. We compile localized regulatory information and use high-resolution data to present multiple siting regimes covering relatively constrained to unconstrained potentials. Our efforts reveal high sensitivity to these variables and sizable uncertainty in the overall wind energy resource potential. Specifically, we find that siting constraints may shift the total capacity available to commercial wind energy by 2.3–15.1 TW. Furthermore, our results illustrate that technology advancement could require larger setbacks from buildings and infrastructure, reducing the total available capacity potential by 20% relative to estimates using current technology, but that this reduction is largely offset by increased generation such that the net effect on generation is 1%. The observed sensitivity to and uncertainty resulting from the variables we analyze suggest there is value in continued study and development of increasingly sophisticated approaches to characterizing wind resource potential.

14 SOLAR ENERGY↗

DOE OSTI2025

Understanding the impacts of inorganic species in woody biomass for preprocessing and pyrolysis–A review

Woody biomass represents an abundant resource for sustainable biofuels, biochemicals, and bioproducts. Technologies for converting woody biomass have been established for decades, and research consistently highlights the critical role of inorganic species and ash plays in feedstock handling and conversion processes, including equipment plugging, corrosion, and catalyst deactivation. A thorough understanding of the variability, transport behavior, and downstream impact of inorganic species in woody biomass is essential for defining feedstock quality specifications and developing effective management strategies for conversion processes. This review compiles critical information in five main sections: 1) inorganic species concentration in woody biomass, based on anatomical fractions and their sources of variability; 2) technique features for quantifying inorganic elemental chemical analysis; 3) impacts of inorganic species on biomass preprocessing; 4) impacts of inorganic species on pyrolysis, and 5) mitigation strategies. Additionally, this review explores future challenges and opportunities in addressing the impacts of inorganic species on biomass quality. These insights aim to support the sustainable development of the biomass-to-bioenergy pipeline and ensure high-quality lignocellulosic feedstocks for efficient downstream conversions. The findings offer valuable guidance to policy makers, industry stakeholders, and researchers in developing effective strategies for managing inorganic species in woody biomass and fostering the sustainable processes for lignocellulosic biorefineries.

09 BIOMASS FUELS↗

DOE OSTI2025

Advanced defrosting techniques in air source heat pumps: A review of vapor injection, thermal energy storage, and experimental frost accumulation data

Electrification is a critical step for reducing greenhouse gas emissions from heating. Air source heat pumps (ASHPs) are a promising alternative to fossil fuel-based systems due to their high coefficients of performance (COP), dual heating and cooling capability, and lower carbon footprint. However, for ASHPs to achieve widespread adoption, they must operate reliably across all climates, including cold regions. Additionally, defrosting techniques should be energy efficient and minimally disruptive to indoor comfort. Vapor injection (VI) technology can address the high-pressure and high-temperature lift challenges encountered in low ambient conditions. More recently, in addition to enhancing heating performance, VI has also been shown to improve the speed and efficiency of reverse cycle defrosting. Likewise, thermal energy storage (TES) has steadily gained attention for its ability to serve as an auxiliary heat source during both normal operation and defrosting. This review analyzes the benefits and limitations of VI- and TES-assisted defrosting approaches. While both technologies show strong potential individually, no studies to date have explored their combined use in ASHP systems. Additionally, to support continued development of defrosting strategies, both in modeling and experimental work, it is critical to establish frost accumulation data under a range of operating conditions. By compiling the available data from the literature, this paper also highlights the limited availability of such experimental data and the wide variation in frosting and defrosting durations and termination criteria, which are often influenced by system design and test setups.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2015

Contribution to the risk characterization of ciguatoxins: LOAEL estimated from eight ciguatera fish poisoning events in Guadeloupe (French West Indies)

From 2010 to 2012, 35 ciguatera fish poisoning (CFP) events involving 87 individuals who consumed locally-caught fish were reported in Guadeloupe (French West Indies). For 12 of these events, the presence of ciguatoxins (CTXs) was indicated in meal remnants and in uncooked fish by the mouse bioassay (MBA). Caribbean ciguatoxins (C-CTXs) were confirmed by liquid chromatography–tandem mass spectrometry (LC–MS/MS) analysis. Using a cell-based assay (CBA), and the only available standard Pacific ciguatoxin-1 (P-CTX-1), the lowest toxins level detected in fish samples causing CFP was 0.022 µg P-CTX-1 equivalent (eq.)·kg{sup −1} fish. Epidemiological and consumption data were compiled for most of the individuals afflicted, and complete data for establishing the lowest observable adverse effects level (LOAEL) were obtained from 8 CFP events involving 21 individuals. Based on toxin intakes, the LOAEL was estimated at 4.2 ng P-CTX-1 eq./individual corresponding to 48.4 pg P-CTX-1 eq. kg{sup −1} body weight (bw). Although based on limited data, these results are consistent with the conclusions of the European Food Safety Authority (EFSA) opinion which indicates that a level of 0.01 µg P-CTX-1 eq. kg{sup −1} fish, regardless of source, should not exert effects in sensitive individuals when consuming a single meal. The calculated LOAEL is also consistent with the U.S. Food and Drug Administration guidance levels for CTXs (0.1 µg C-CTX-1 eq. kg{sup −1} and 0.01 µg P-CTX-1 eq. kg{sup −1} fish). - Highlights: • We report on an epidemiological study on Ciguatera events in the French West Indies. • The collection of consumption data allows for the first time the LOAEL determination. • The LOAEL for ciguatoxins was established at 48.4 pg P-CTX-1 eq. kg{sup −1} bw. • LC–MS/MS provided structural confirmation of C-CTX1 in two suspected samples • Neuro-2A CBA is suitable for assessing composite toxicity levels in fish samples.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

The clumped-isotope geochemistry of exhumed marbles from Naxos, Greece

Exhumation and accompanying retrograde metamorphism alter the compositions and textures of metamorphic rocks through deformation, mineral–mineral reactions, water–rock reactions, and diffusion-controlled intra- and inter-mineral atomic mobility. Here, we demonstrate that these processes are recorded in the clumped- and single-isotope (δ 13 C and δ 18 O) compositions of marbles, which can be used to constrain retrograde metamorphic histories. We collected 27 calcite and dolomite marbles along a transect from the rim to the center of the metamorphic core-complex of Naxos (Greece), and analyzed their carbonate single- and clumped-isotope compositions. The majority of Δ 47 values of whole-rock samples are consistent with exhumation- controlled cooling of the metamorphic complex. However, the data also reveal that water–rock interaction, deformation driven recrystallization and thermal shock associated with hydrothermal alteration may considerably impact the overall distribution of Δ 47 values. We analyzed specific carbonate fabrics influenced by deformation and fluid–rock reaction to study how these processes register in the carbonate clumped-isotope system. Δ 47 values of domains drilled from a calcite marble show a bimodal distribution. Low Δ 47 values correspond to an apparent temperature of 260 °C and are common in static fabrics; high Δ 47 values correspond to an apparent temperature of 200 °C and are common in dynamically recrystallized fabrics. We suggest that the low Δ 47 values reflect diffusion-controlled isotopic reordering during cooling, whereas high Δ 47 values reflect isotopic reordering driven by dynamic recrystallization. We further studied the mechanism by which dynamic recrystallization may alter Δ 47 values by controlled heating experiments. Results show no significant difference between laboratory reactions rates in the static and dynamic fabrics, consistent with a mineral-extrinsic mechanism, in which slip along crystal planes was associated with atomic-scale isotopic reordering in the calcite lattice. An intrinsic mechanism (enhanced isotopic reordering rate in deformed minerals) is contraindicated by these experiments. We suggest that Δ 47 values of dynamically recrystallized fabrics that form below the diffusion-controlled blocking-temperature for calcite constrain the temperature of deformation. We find that Δ 47 -based temperatures of static fabrics from Naxos marbles are ~60–80 °C higher than commonly observed in slowly cooled metamorphic rocks, and would suggest cooling rates of ~10 5 °CMyr -1 . A similar thermal history is inferred for dolomite marbles from the core vicinity, which preserve apparent temperatures up to 200 °C higher than a typical blocking temperature (~300 °C). This finding could be explained by a hydrothermal event driving a brief thermal pulse and locally resetting Δ 47 values. Rapid cooling of the core-complex region is consistent with a compilation of published cooling ages and a new apatite U–Th/He age, associating the thermal event with the emplacement of a granodiorite pluton at ~12 Ma.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗