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DOE OSTI2022

Artificial Intelligence/Machine Learning Technologies for Advanced Reactors (Workshop Summary Report)

A workshop on artificial intelligence and machine learning (AI/ML) for advanced reactors (AR) was held October 5-6, 2021. The workshop was to be attended in-person at ANL but COVID restrictions forced the workshop to go virtual. The objectives of the workshop were to identify the most promising AI/ML opportunities for improving advanced reactor design, optimizing plant performance, and enhancing economic competitiveness and to develop an understanding of the scientific, engineering and licensing challenges facing their application. The workshop planning committee included GAIN, EPRI and NEI and members of three national laboratories (ANL, INL, and ORNL). The workshop was attended by more than 200 individuals representing academic and scientific institutions and the nuclear power industry. The definition put forth for an AI/ML system was one that perceives its environment and takes actions that maximize its chance of achieving its goals. In this report AI/ML refers to next generation algorithms that include deep learning, statistical analysis and data analytics and associated scientific computing and their potential application to the design, licensing, operation and maintenance of ARs. These methods typically incorporate models built from process data and may also include data generated by simulations that represent the behavior of a system. The workshop was organized in response to the growing interest in application of AI/ML for improving the economic competitiveness of nuclear energy. Increasingly more resources are being allocated to investigating the benefits of AI/ML methods. The DOE created the Artificial Intelligence & Technology Office to promote their development. And within the Office of Nuclear Energy, resources have been allocated to explore and understand the potential benefits of AI/ML. Additionally, the national laboratories are strategically positioned with DOE computing facilities such as Summit, Perlmutter, Aurora and Frontier that support large-scale simulations, hybrid HPC models with AI surrogates, and the exploration of new types of generative models emerging from multi-model data streams and sources. The workshop was organized with members of the AR community to understand the effort and to identify the level of interest and progress in this emerging technology. The workshop discussions focused on identifying opportunities for AI/ML across diverse areas of the nuclear industry and identifying current scientific and engineering challenges for advanced reactors that might be addressed through transformational uses of AI/ML. Discussion panels focused on four high-interest technical domains for advanced reactors: design, maintenance and operations, energy storage, and materials. The results of those discussions are summarized in this report. This includes opportunities that were identified for exploiting AI techniques and methods to improve the efficacy and efficiency of reactor analysis and to improve the operation and optimization of advanced reactors. Advanced reactor developers expressed an interest in learning more about AI/ML methods and their application. This included understanding whether ML methods can provide an advantage over existing nonlinear data regression methods for collapsing high-fidelity simulation results into faster running models. A consensus emerged that AR advances planned for the next decade will benefit from the use of AI/ML tools. The need exists to understand and model complex systems across length scales and modalities. AI/ML is a tool for discovery that can yield a set of engineering principles for use by nuclear engineers, licensing bodies, and operators to solve problems in plant design, safety analyses, autonomous operation, and predictive maintenance. While AI/ML represents a new set of tools, an awareness by the nuclear community of the full potential is still in the early stages so there is a need to increase awareness. It appears that the wide-spread adoption of AI/ML tools for ARs would be facilitated by future educational workshops that describe foundational methods and capabilities and describe successful applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Skewing the CMB×LSS: a fast method for bispectrum analysis

Upcoming cosmic microwave background (CMB) lensing measurements and tomographic galaxy surveys are expected to provide us with high-precision data sets in the coming years, thus paving the way for fruitful cross-correlation analyses. Here, in this paper we study the information content of the weighted skew-spectrum, a nearly-optimal estimator of the angular bispectrum amplitude, as a means to extract non-Gaussian information on both bias and cosmological parameters from the bispectra of galaxies cross-correlated with CMB lensing, while gaining significantly on speed. Our results show that for the combination of the Planck satellite and the Dark Energy Spectroscopic Instrument (DESI), the difference in the constraints on bias and cosmological parameters from the skew-spectrum and the bispectrum is at most 17%. We further compare and find agreement between our theoretical skew-spectra and those estimated from N-body simulations, for which it is important to include gravitational non-linearities beyond perturbation theory and the post-Born effect for CMB lensing. We define an algorithm to apply the skew-spectrum estimator to the data and, as a preliminary step, we use the skew-spectra to constrain bias parameters and the amplitude of shot noise from the simulations through a Markov chain Monte Carlo likelihood analysis, finding that it may be possible to reach percent-level estimates for the linear bias parameter b 1 .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: A comparison with radiologist-based assessments

Purpose: To develop a semiautomated computer-aided diagnosis (CAD) system for thyroid cancer using two-dimensional ultrasound images that can be used to yield a second opinion in the clinic to differentiate malignant and benign lesions. Methods: A total of 118 ultrasound images that included axial and longitudinal images from patients with biopsy-confirmed malignant (n = 30) and benign (n = 29) nodules were collected. Thyroid CAD software was developed to extract quantitative features from these images based on thyroid nodule segmentation in which adaptive diffusion flow for active contours was used. Various features, including histogram, intensity differences, elliptical fit, gray-level co-occurrence matrixes, and gray-level run-length matrixes, were evaluated for each region imaged. Based on these imaging features, a support vector machine (SVM) classifier was used to differentiate benign and malignant nodules. Leave-one-out cross-validation with sequential forward feature selection was performed to evaluate the overall accuracy of this method. Additionally, analyses with contingency tables and receiver operating characteristic (ROC) curves were performed to compare the performance of CAD with visual inspection by expert radiologists based on established gold standards. Results: Most univariate features for this proposed CAD system attained accuracies that ranged from 78.0% to 83.1%. When optimal SVM parameters that were established using a grid search method with features that radiologists use for visual inspection were employed, the authors could attain rates of accuracy that ranged from 72.9% to 84.7%. Using leave-one-out cross-validation results in a multivariate analysis of various features, the highest accuracy achieved using the proposed CAD system was 98.3%, whereas visual inspection by radiologists reached 94.9% accuracy. To obtain the highest accuracies, “axial ratio” and “max probability” in axial images were most frequently included in the optimal feature sets for the authors’ proposed CAD system, while “shape” and “calcification” in longitudinal images were most frequently included in the optimal feature sets for visual inspection by radiologists. The computed areas under curves in the ROC analysis were 0.986 and 0.979 for the proposed CAD system and visual inspection by radiologists, respectively; no significant difference was detected between these groups. Conclusions: The use of thyroid CAD to differentiate malignant from benign lesions shows accuracy similar to that obtained via visual inspection by radiologists. Thyroid CAD might be considered a viable way to generate a second opinion for radiologists in clinical practice.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Optimal dimensionality selection for independent component analysis of transcriptomic data

Independent component analysis is an unsupervised machine learning algorithm that separates a set of mixed signals into a set of statistically independent source signals. Applied to high-quality gene expression datasets, independent component analysis effectively reveals both the source signals of the transcriptome as co-regulated gene sets, and the activity levels of the underlying regulators across diverse experimental conditions. Two major variables that affect the final gene sets are the diversity of the expression profiles contained in the underlying data, and the user-defined number of independent components, or dimensionality, to compute. Availability of high-quality transcriptomic datasets has grown exponentially as high-throughput technologies have advanced; however, optimal dimensionality selection remains an open question. We computed independent components across a range of dimensionalities for four gene expression datasets with varying dimensions (both in terms of number of genes and number of samples). We computed the correlation between independent components across different dimensionalities to understand how the overall structure evolves as the number of user-defined components increases. We then measured how well the resulting gene clusters reflected known regulatory mechanisms, and developed a set of metrics to assess the accuracy of the decomposition at a given dimension. We found that over-decomposition results in many independent components dominated by a single gene, whereas under-decomposition results in independent components that poorly capture the known regulatory structure. From these results, we developed a new method, called OptICA, for finding the optimal dimensionality that controls for both over- and under-decomposition. Specifically, OptICA selects the highest dimension that produces a low number of components that are dominated by a single gene. We show that OptICA outperforms two previously proposed methods for selecting the number of independent components across four transcriptomic databases of varying sizes. OptICA avoids both over-decomposition and under-decomposition of transcriptomic datasets resulting in the best representation of the organism’s underlying transcriptional regulatory network.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Finding New Cell Wall Regulatory Genes in Populus trichocarpa Using Multiple Lines of Evidence

Understanding the regulatory network controlling cell wall biosynthesis is of great interest in Populus trichocarpa, both because of its status as a model woody perennial and its importance for lignocellulosic products. We searched for genes with putatively unknown roles in regulating cell wall biosynthesis using an extended network-based Lines of Evidence (LOE) pipeline to combine multiple omics data sets in P. trichocarpa, including gene coexpression, gene comethylation, population level pairwise SNP correlations, and two distinct SNP-metabolite Genome Wide Association Study (GWAS) layers. By incorporating validation, ranking, and filtering approaches we produced a list of nine high priority gene candidates for involvement in the regulation of cell wall biosynthesis. We subsequently performed a detailed investigation of candidate gene GROWTH-REGULATING FACTOR 9 (PtGRF9). To investigate the role of PtGRF9 in regulating cell wall biosynthesis, we assessed the genome-wide connections of PtGRF9 and a paralog across data layers with functional enrichment analyses, predictive transcription factor binding site analysis, and an independent comparison to eQTN data. Our findings indicate that PtGRF9 likely affects the cell wall by directly repressing genes involved in cell wall biosynthesis, such as PtCCoAOMT and PtMYB.41, and indirectly by regulating homeobox genes. Furthermore, evidence suggests that PtGRF9 paralogs may act as transcriptional co-regulators that direct the global energy usage of the plant. Using our extended pipeline, we show multiple lines of evidence implicating the involvement of these genes in cell wall regulatory functions and demonstrate the value of this method for prioritizing candidate genes for experimental validation.

network analysis↗
DOE OSTI2023

Dancing the Quantum Waltz: Compiling Three-Qubit Gates on Four Level Architectures

Superconducting quantum devices are a leading technology for quantum computation, but they face several challenges. Gate errors, coherence errors and a lack of connectivity all contribute to low fidelity results. In particular, connectivity restrictions enforce a gate set that requires three-qubit gates to be decomposed into one- or two-qubit gates. This substantially increases the number of two-qubit gates that need to be executed. However, many quantum devices have access to higher energy levels. We can expand the qubit abstraction of |0〉 and |1〉 to a ququart which has access to the |2〉 and |3〉 state, but with shorter coherence times. This allows for two qubits to be encoded in one ququart, enabling increased virtual connectivity between physical units from two adjacent qubits to four fully connected qubits. This connectivity scheme allows us to more efficiently execute three-qubit gates natively between two physical devices.We present direct-to-pulse implementations of several three-qubit gates, synthesized via optimal control, for compilation of three-qubit gates onto a superconducting-based architecture with access to four-level devices with the first experimental demonstration of four-level ququart gates designed through optimal control. We demonstrate strategies that temporarily use higher level states to perform Toffoli gates and always use higher level states to improve fidelities for quantum circuits. We find that these methods improve expected fidelities with increases of 2x across circuit sizes using intermediate encoding, and increases of 3x for fully-encoded ququart compilation.

Litteken, Andrew↗
DOE OSTI2015

Durable Airtightness in Single-Family Dwellings: Field Measurements

Durability of building envelope is important to new homes that are increasingly built with improved levels of airtightness. It is also important to weatherized homes such that energy savings from retrofit measures, such as air sealing, are persistent. We presented a comparison of air leakage measurements collected in November 2013 through March 2014, with two sets of prior data collected between 2001-2003 from 17 new homes located near Atlanta, GA, and 17 homes near Boise, ID that were weatherized in 2007-2008. The purpose of the comparison is to determine if there are changes to the airtightness of building envelopes over time. The air leakage increased in all but one of the new homes, with a mean increase of about 25%. The weatherized homes also showed an increase in the mean air leakage (12%). We performed a regression analysis to describe the relationship between prior and current measurements in terms of normalized leakage (NL). The best estimate of the aging factor predicts a 15% increase in NL over ten years. Further analysis using ResDB data (LBNL’s Residential Diagnostic Database) showed the expected changes in air leakage if aging were modeled. These results imply that we should examine the causes of increased leakage and methods to avoid them. This increase in leakage with time should be accounted for in long-term population-wide energy savings estimates, such as those used in ratings or energy savings programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Development of SiC-based Cladding for Accident Tolerant Fuels

Fuels and core structures in current light water reactors (LWR's) are vulnerable to catastrophic failure in severe accidents. This vulnerability is attributed primarily to the rapid oxidation kinetics of zirconium alloys in a water vapor environment at very high temperatures. Alternative materials to zirconium alloys with reduced oxidation kinetics are sought to enable enhanced accident-tolerant fuels and cores. Among the candidate alternatives, silicon carbide (SiC) - based materials, in particular continuous SiC fiber-reinforced SiC matrix ceramic composites (SiC/SiC composites), are considered to offer outstanding safety benefits. The primary objective of this work is to provide with updated overviews of the concepts, development status, and future prospect for the SiC-based accident-tolerant fuel (ATF) claddings. A systematic technology evaluation program plan was developed and updated to review the technologies needed, to identify and analyze critical gaps, and to lay out a road-map toward establishing key technologies. The technology review and the gap analysis in this exercise were based on an early study and community workshops. Many of the technical gaps identified are related with the three key feasibility issues: hydrothermal corrosion, mechanical failure, and fuel compatibility. Additional performance issues including accident-tolerance features and fission product retention during the normal operation have also been identified. The program plan was designed to systematically address the key gap issues and is formulated in a work breakdown structure. Simultaneously, the plan is being established to set a technical program for advancing the technological readiness levels of concept non-specific essential technologies in three top-level categories of Design and Failure, Environmental Effects, and Off-normal Behavior. The execution of the program plan is in progress across the U.S. Department of Energy laboratories, industries, universities, and international collaborators. The critical issues for SiC composite-based fuel cladding concepts were found to be related to the performance in the normal operation. In particular, radiation-assisted hydrothermal corrosion and the gaseous fission product (FP) retention are identified to be the most critical issues. The primary ORNL concept pursues the use of dual-purpose coating on the SiC composite cladding wall to mitigate the corrosion and the gas permeation issues at the same time, whereas the alternative approaches such as the dense monolithic SiC protective layer combined with the water chemistry control may be feasible. Surface metallization through variations of physical vapor deposition and electroplating are being demonstrated. The FP gas retention is an issue associated with the cracking for the ceramic matrix composite cladding. To establish the probabilistic failure behavior, the relevant test methods were developed in ASTM Committee C28 on Advanced Ceramics. Toward the end-plugging technology development, various methods for joining SiC to SiC had been developed and tested for mechanical properties, irradiation effects, and environmental effects. Multiple methods including the pressureless transient eutectic SiC joining and CVD-based SiC joining were identified particularly promising. In addition, a multi-physics, three-dimensional finite element-based modeling is in progress toward an integrated simulation of the SiC composite-cladded fuel evolutions. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

Building America Case Study: Low-Cost Evaluation of Energy Savings at the Community Scale, Fresno, California (Fact Sheet)

A new construction pilot community was constructed by builder-partner Wathen-Castanos Hybrid Homes (WCHH) based on a single occupied test house that was designed to achieve greater than 30% energy savings with respect to the House Simulation Protocols (Hendron, Robert; Engebrecht, Cheryn (2010). Building America House Simulation Protocols. Golden, CO: National Renewable Energy Laboratory.). Builders face several key problems when implementing a whole-house systems integrated measures package (SIMP) from a single test house into multiple houses. Although a technical solution already may have been evaluated and validated in an individual test house, the potential exists for constructability failures at the community scale. This report addresses factors of implementation and scalability at the community scale and proposes methodologies by which community-scale energy evaluations can be performed based on results at the occupied test house level. Research focused on the builder and trade implementation of a SIMP and the actual utility usage in the houses at the community scale of production. Five occupants participated in this community-scale research by providing utility bills and information on occupancy and miscellaneous gas and electric appliance use for their houses. IBACOS used these utility data and background information to analyze the actual energy performance of the houses. Verification with measured data is an important component in predictive energy modeling. The actual utility bill readings were compared to projected energy consumption using BEopt with actual weather and thermostat set points for normalization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Durable Airtightness in Single-Family Dwellings - Field Measurementsand Analysis

Here, durability of the building envelope is important to new homes that are increasingly built with improved levels of airtightness. It is also important to weatherized homes such that energy savings from retrofit measures, such as air sealing, are persistent. This paper presents a comparison of air leakage measurements collected in November 2013 through March 2014, with two sets of prior data collected between 2001-2003 from 17 new homes located near Atlanta, GA, and 17 homes near Boise, ID that were weatherized in 2007- 2008. The purpose of the comparison is to determine if there are changes to the airtightness of building envelopes over time. The air leakage increased in all but one of the new homes, with a mean increase of about 25%. The weatherized homes also showed an increase in the mean air leakage (12%). A regression analysis was performed to describe the relationship between prior and current measurements in terms of normalized leakage (NL). The best estimate of the ageing factor predicts a 15% increase in NL over ten years. Further analysis using ResDB data (LBNL’s Residential Diagnostic Database) showed the expected changes in air leakage if ageing were modelled. These results imply the need to examine the causes of increased leakage and methods to avoid them. This increase in leakage with time should be accounted for in long-term population-wide energy savings estimates, such as those used in ratings or energy savings programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

A correction for the Hartree-Fock density of states for jellium without screening

We revisit the Hartree-Fock (HF) calculation for the uniform electron gas, or jellium model, whose predictions—divergent derivative of the energy dispersion relation and vanishing density of states (DOS) at the Fermi level—are in qualitative disagreement with experimental evidence for simple metals. Currently, this qualitative failure is attributed to the lack of screening in the HF equations. Employing Slater’s hyper-Hartree-Fock (HHF) equations, derived variationally, to study the ground state and the excited states of jellium, we find that the divergent derivative of the energy dispersion relation and the zero in the DOS are still present, but shifted from the Fermi wavevector and energy of jellium to the boundary between the set of variationally optimised and unoptimised HHF orbitals. The location of this boundary is not fixed, but it can be chosen to lie at arbitrarily high values of wavevector and energy, well clear from the Fermi level of jellium. We conclude that, rather than the lack of screening in the HF equations, the well-known qualitative failure of the ground-state HF approximation is an artifact of its nonlocal exchange operator. Other similar artifacts of the HF nonlocal exchange operator, not associated with the lack of electronic correlation, are known in the literature.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Automated detector simulation and reconstruction parametrization using machine learning

Rapidly applying the effects of detector response to physics objects (e.g. electrons, muons, showers of particles) is essential in high energy physics. Presently available tools for the transformation from truth-level physics objects to reconstructed detector-level physics objects involve manually defining resolution functions. These resolution functions are typically derived in bins of variables that are correlated with the resolution (e.g. pseudorapidity and transverse momentum). This process is time consuming, requires manual updates when detector conditions change, and can miss important correlations. Machine learning offers a way to automate the process of building these truth-to-reconstructed object transformations and can capture complex correlation for any given set of input variables. Such machine learning algorithms, with sufficient optimization, could have a wide range of applications: improving phenomenological studies by using a better detector representation, allowing for more efficient production of Geant4 simulation by only simulating events within an interesting part of phase space, and studies on future experimental sensitivity to new physics.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Understanding and Improving Energy Efficiency of Regional Mobility Systems Leveraging System-Level Data

Increased congestion required urban Americans to travel 6.8 billion hours more and purchase 3.1 billion gallons of fuel for a congestion cost of $\$$153 billion, according to the 2019 Urban Mobility Report. How to effectively manage the regional mobility system and improve the energy efficiency presents a big challenge to public agencies. Recent years have witnessed massive multi-jurisdictional multi-modal system-level data from various sources, which provides an unprecedented opportunity to improve the mobility system and its energy efficiency. However, implications of system-level data for mobility and energy efficiency are unclear. Those system-level data sets are siloed, spatially and temporally sparse, biased, not unified, and lacking of insights for system management. Consequently, there is a real need to acquire, fuse, mine and learn from multi-source system-level data to prepare public agencies to deal more effectively with large-scale energy efficiency modeling, management and planning. This project proposes to intensively review inexpensive, replicable and openly-accessible data from multi-modal systems, develop a data-driven system-level modeling framework enabled and validated by data, identify the energy inefficiencies of mobility systems from infrastructure, vehicles, passenger systems, and quantify the benefits of system-level strategies to improve mobility/energy efficiency. In addition, this research develops models to effectively estimate energy consumption and emissions from various types of vehicles on the roadway networks, with high granularity and high fidelity. Traditional models often heavily rely on aggregated infrastructure or vehicle/passenger data, for example, the census survey, land-use, and traffic counts of one or several classes, which may lead to research gaps considering the emerging vehicle technologies. Those models do not contain individual vehicular information. We propose an integrated data-driven method that combines multiple network modeling components, featuring the utilization of state-wide vehicle registration data. The additional vehicle registration data improve the model performance, and produce high-resolution vehicle-specific estimates of emissions and network performance metrics. Two case studies on the Pittsburgh and Philadelphia regional network show that the proposed method can efficiently and effectively estimate the emissions of a large-scale network, and provide valuable information for evaluating common management strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

initMIP-Antarctica: an ice sheet model initialization experiment of ISMIP6

Ice sheet numerical modeling is an important tool to estimate the dynamic contribution of the Antarctic ice sheet to sea level rise over the coming centuries. The influence of initial conditions on ice sheet model simulations, however, is still unclear. To better understand this influence, an initial state intercomparison exercise (initMIP) has been developed to compare, evaluate, and improve initialization procedures and estimate their impact on century-scale simulations. initMIP is the first set of experiments of the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6), which is the primary Coupled Model Intercomparison Project Phase 6 (CMIP6) activity focusing on the Greenland and Antarctic ice sheets. Following initMIP-Greenland, initMIP-Antarctica has been designed to explore uncertainties associated with model initialization and spin-up and to evaluate the impact of changes in external forcings. Starting from the state of the Antarctic ice sheet at the end of the initialization procedure, three forward experiments are each run for 100 years: a control run, a run with a surface mass balance anomaly, and a run with a basal melting anomaly beneath floating ice. This study presents the results of initMIP-Antarctica from 25 simulations performed by 16 international modeling groups. The submitted results use different initial conditions and initialization methods, as well as ice flow model parameters and reference external forcings. We find a good agreement among model responses to the surface mass balance anomaly but large variations in responses to the basal melting anomaly. These variations can be attributed to differences in the extent of ice shelves and their upstream tributaries, the numerical treatment of grounding line, and the initial ocean conditions applied, suggesting that ongoing efforts to better represent ice shelves in continental-scale models should continue.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

PV Degradation Methodology Comparison – A Basis for a Standard

What is the best method to determine long-term PV system performance and degradation rates? Ideally, one universally applicable methodology would be desirable so that a single number could be derived. However, data sets vary in their attributes and evidence is presented that defining two methodologies may be preferable. Monte Carlo simulations of artificial performance data allowed investigation of different methodologies and their respective confidence intervals. Tradeoffs between different approaches were delineated, elucidating as to why two separate approaches may need to be included in a standard. Regression approaches tend to be preferable when data sets are less contaminated by seasonality, noise and occurrence of outliers although robust regression can significantly improve the accuracy when outliers are present. In the presence of outliers, marked seasonality, or strong soiling events, year-on-year approaches tend to outperform regression approaches.

14 SOLAR ENERGY↗
DOE OSTI2017

Adaptive quantum/molecular mechanics: what have we learned, where are we, and where do we go from here?: Adaptive QM/MM

Adaptive quantum-mechanics/molecular-mechanics (QM/MM) methods feature on-the-fly reclassification of atoms as QM or MM during a molecular dynamics(MD) simulation, allowing the location and contents of the QM subsystem to be dynamically updated as needed. Such flexibility is a distinct advantage over conventional QM/MM, where a ‘static’ boundary is retained between the QM and MM subsystems. The ‘dynamic’ boundary in adaptive QM/MM allows a finite-size QM to sustain simulations with an arbitrary length of time. Here, to ensure smooth transitions between QM and MM, the energy or forces are interpolated.Special treatments are applied so that artifacts are eliminated or minimized.Recent developments have shed light on the relationship between the adaptive algorithms that describe Hamiltonian and non-Hamiltonian systems. Originally developed to model an ion solvated in bulk solvent, adaptive QM/MM has been enhanced in many aspects, including the treatment of molecular fragments in macromolecules, monitoring molecules entering/leaving binding sites, and tracking proton transfer via the Grotthuss mechanism. Because the size of the QM region can be set as small as possible in adaptive QM/MM, the computational costs can be kept low. Small QM subsystems also facilitate the utilization of high-level QM theory and long simulation time, which can potentially lead to new insights.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Coupled-cluster based approach for core-level states in condensed phase: Theory and application to different protonated forms of aqueous glycine

A theoretical approach for calculating core-level states in condensed phase is presented. The approach is based on equation-of-motion coupled-cluster theory (EOMCC) and effective fragment potential (EFP) method. By introducing an approximate treatment of double excitations in the EOM-CCSD (EOM-CC with single and double substitutions) ansatz, we address poor convergence issues that are encountered for the core-level states and significantly reduce computational costs. While the approximations introduce relatively large errors in the absolute values of transition energies, the errors are systematic. Consequently, chemical shifts, changes in ionization energies relative to reference systems, are reproduced reasonably well. By using different protonation forms of solvated glycine as a benchmark system, we show that our protocol is capable of reproducing the experimental chemical shifts with a quantitative accuracy. The results demonstrate that chemical shifts are very sensitive to the solvent interactions and that explicit treatment of solvent, such as EFP, is essential for achieving quantitative accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

SU-E-J-53: A Phantom Design to Assist Patient Position Verification System in Daily Image-Guided RT and Comprehensive QA Measurements

Purpose This study is to implement a homemade novel device with surface locking couch index to check daily radiograph (DR) function of adaPTInsight™, stereoscopic image guided system (SIGS), for proton therapy. The comprehensive daily QA checks of proton pencil beam output, field size, flatness and symmetry of spots and energy layers will be followed by using MatriXX dosimetry device. Methods The iBa MatriXX device was used to perform daily dosimetry which is also used to perform SIGS checks. A set of markers were attached to surface of MatriXX device in alignment of DRR of reconstructed CT images and daily DR. The novel device allows MatriXX to be fit into the cradle which was locked by couch index bars on couch surface. This will keep the MatriXX at same XY plane daily with exact coordinates. Couch height Z will be adjusted according to imaging to check isocenter-laser coincidence accuracy. Results adaPTInsight™ provides robotic couch to move in 6-degree coordinate system to align the dosimetry device to be within 1.0 mm / 1.0°. The daily constancy was tightened to be ± 0.5 mm / 0.3° compared to 1.0 mm / 1.0° before. For gantry at 0° and couch all 0° angles (@ Rt ARM 0 setting), offsets measured of the couch systems were ≤ 0.5° in roll, yaw and pitch dimensions. Conclusion Simplicity of novel device made daily image guided QA consistent with accuracy. The offset of the MatriXX isocenter-laser coincident was reproducible. Such easy task not only speeds up the setup, but it increases confidence level in detailed daily comprehensive measurements. The total SIGS alignment time has been shortened with less setup error. This device will enhance our experiences for the future QA when cone beam CT imaging modality becomes available at proton therapy center.

60 APPLIED LIFE SCIENCES↗

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