Even More Efficient Quantum Computations of Chemistry Through Tensor Hypercontraction
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71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Joint Real-Time Energy and Demand-Response Management using a Hybrid Coalitional-Noncooperative Game
In order to model the interactions among utility companies, building demands and renewable energy generators (REGs), a hybrid coalitional-noncooperative game framework has been proposed. We formulate a dynamic non-cooperative game to study the energy dispatch within multiple utility companies, while we take a coalitional perspective on REGs and buildings demands through a hedonic coalition formation game approach. In this case, building demands request different power supply from REGs, then the building demands can be organized into an ultimate coalition structure through a distributed hedonic shift algorithm. At the same time, utility companies can also obtain a stable power generation profile. In addition, the interactive progress among the utility companies and building demands which cannot be supplied by REGs is implemented by distributed game theoretic algorithms. Numerical results illustrate that the proposed hybrid coalitional-noncooperative game scheme reduces the cost of both building demands and utility companies compared with the initial scene.
24 POWER TRANSMISSION AND DISTRIBUTION↗
A Communication-Avoiding 3D LU Factorization Algorithm for Sparse Matrices
We propose a new algorithm to improve the strong scalability of right-looking sparse LU factorization on distributed memory systems. Our 3D sparse LU algorithm uses a three-dimensional MPI process grid, aggressively exploits elimination tree parallelism and trades off increased memory for reduced per-process communication. We also analyze the asymptotic improvements for planar graphs (e.g., from 2D grid or mesh domains) and certain non-planar graphs (specifically for 3D grids and meshes). For planar graphs with n vertices, our algorithm reduces communication volume asymptotically in n by a factor of O{log n} and latency by a factor of O{log n}. For non-planar cases, our algorithm can reduce the per-process communication volume by 3× and latency by O{n^1/3} times. In all cases, the memory needed to achieve these gains is a constant factor. We implemented our algorithm by extending the 2D data structure used in superLU. Our new 3D code achieves speedups up to 27× for planar graphs and up to 3.3× for non-planar graphs over the baseline 2D superLU when run on 24,000 cores of a Cray XC30.
Sao, Piyush↗
Self-Taught Anomaly Detection With Hybrid Unsupervised/Supervised Machine Learning in Optical Networks
Here this paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised machine learning scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM enables a self-learning capability that eliminates the requirement of prior knowledge of abnormal network behaviors and therefore can potentially detect unforeseen anomalies. Second, we introduce a self-taught mechanism that transfers the patterns learned by the DCM to a supervised data regression and classification module (DRCM). The DRCM, whose complexity is mainly related to the scale of the applied supervised learning model, can potentially facilitate more scalable and time-efficient online anomaly detection by avoiding excessively traversing the original dataset. We designed the DCM and DRCM based on the density-based clustering algorithm and the deep neural network structure, respectively. Evaluations with experimental data from two use cases (i.e., single-point detection and end-to-end detection) demonstrate that up to 99% anomaly detection accuracy can be achieved with a false positive rate below 1%.
42 ENGINEERING↗
Description of reaction and vibrational energetics of CO 2 –NH 3 interaction using quantum computing algorithms
CO 2 capture is critical to solving global warming. Amine-based solvents are extensively used to chemically absorb CO 2 . Thus, it is crucial to study the chemical absorption of CO 2 by amine-based solvents to better understand and optimize CO 2 capture processes. Here, we use quantum computing algorithms to quantify molecular vibrational energies and reaction pathways between CO 2 and a simplified amine-based solvent model—NH 3 . Molecular vibrational properties are important to understanding kinetics of reactions. However, the molecule size correlates with the strength of anharmonicity effect on vibrational properties, which can be challenging to address using classical computing. Quantum computing can help enhance molecular vibrational calculations by including anharmonicity. We implement a variational quantum eigensolver (VQE) algorithm in a quantum simulator to calculate ground state vibrational energies of reactants and products of the CO 2 and NH 3 reaction. The VQE calculations yield ground vibrational energies of CO 2 and NH 3 with similar accuracy to classical computing. In the presence of hardware noise, Compact Heuristic for Chemistry (CHC) ansatz with shallower circuit depth performs better than Unitary Vibrational Coupled Cluster. The “Zero Noise Extrapolation” error-mitigation approach in combination with CHC ansatz improves the vibrational calculation accuracy. Excited vibrational states are accessed with quantum equation of motion method for CO 2 and NH 3 . Using quantum Hartree–Fock (HF) embedding algorithm to calculate electronic energies, the corresponding reaction profile compares favorably with Coupled Cluster Singles and Doubles while being more accurate than HF. Our research showcases quantum computing applications in the study of CO 2 capture reactions.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Comparison of the spatial statistics of random and defined-sequence photoresist films
The resolution-line edge roughness-sensitivity tradeoff has motivated the exploration of potential improvements using defined sequence polymers and polymer-bound photoacid generators and quenchers. We characterize the internal structures of positive tone photoresist polymer films formed from defined sequence polymers and compare them with random copolymers of the same composition. We model their imaging to connect initially to developable film structures. We use a polymer packing algorithm to simulate films of diverse compositions and locations of photoacid generators and quenchers, using the composition of an ESCAP photoresist. We use a simple extreme ultraviolet exposure-deprotection algorithm to model developable image formation within them. In all cases, the spatial distribution of chemical moieties in the film for defined sequence polymers is nearly indistinguishable from random copolymers. We evaluate several exposure-deprotection scenarios and find that a defined sequence copolymer has a distinctive developable image under certain circumstances. The use of defined sequence polymers within a photoresist layer does not automatically result in improved imaging; however, they do have some characteristics different from random polymers of the same composition. Further study of these characteristics may provide a route to improved control over the nanoscale imaging process.
36 MATERIALS SCIENCE↗
MO-FG-204-01: Improved Noise Suppression for Dual-Energy CT Through Entropy Minimization
Purpose: In dual energy CT (DECT), noise amplification during signal decomposition significantly limits the utility of basis material images. Since clinically relevant objects contain a limited number of materials, we propose to suppress noise for DECT based on image entropy minimization. An adaptive weighting scheme is employed during noise suppression to improve decomposition accuracy with limited effect on spatial resolution and image texture preservation. Methods: From decomposed images, we first generate a 2D plot of scattered data points, using basis material densities as coordinates. Data points representing the same material generate a highly asymmetric cluster. We orient an axis by minimizing the entropy in a 1D histogram of these points projected onto the axis. To suppress noise, we replace pixel values of decomposed images with center-of-mass values in the direction perpendicular to the optimal axis. To limit errors due to cluster overlap, we weight each data point’s contribution based on its high and low energy CT values and location within the image. The proposed method’s performance is assessed on physical phantom studies. Electron density is used as the quality metric for decomposition accuracy. Our results are compared to those without noise suppression and with a recently developed iterative method. Results: The proposed method reduces noise standard deviations of the decomposed images by at least one order of magnitude. On the Catphan phantom, this method greatly preserves the spatial resolution and texture of the CT images and limits induced error in measured electron density to below 1.2%. In the head phantom study, the proposed method performs the best in retaining fine, intricate structures. Conclusion: The entropy minimization based algorithm with adaptive weighting substantially reduces DECT noise while preserving image spatial resolution and texture. Future investigations will include extensive investigations on material decomposition accuracy that go beyond the current electron density calculations. This work was supported in part by the National Institutes of Health (NIH) under Grant Number R21 EB012700.
60 APPLIED LIFE SCIENCES↗
A Polyhedral Outer-approximation, Dynamic-discretization optimization solver, 1.x
In this software, we implement an adaptive, multivariate partitioning algorithm for solving mixed-integer nonlinear programs (MINLP) to global optimality. The algorithm combines ideas that exploit the structure of convex relaxations to MINLPs and bound tightening procedures
Bent, Rusell↗
Guided design of copper oxysulfide superconductors
We describe a framework for designing novel materials, combining modern first-principles electronic-structure tools, materials databases, and evolutionary algorithms capable of exploring large configurational spaces. Guided by the chemical principles introduced by Antipov et al., for the design and synthesis of the Hg-based high-temperature superconductors, we apply our framework to screen 333 proposed compositions to design a new layered copper oxysulfide, Hg(CaS)2CuO2. We evaluate the prospects of superconductivity in this oxysulfide using theories based on charge-transfer energies, orbital distillation and uniaxial strain.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Topological vulnerability of power grids to disasters: Bounds, adversarial attacks and reinforcement
Natural disasters like hurricanes, floods or earthquakes can damage power grid devices and create cascading blackouts and islands. The nature of failure propagation and extent of damage, among other factors, is dependent on the structural features of the grid, that are distinct from that of random networks. This paper analyzes the structural vulnerability of real power grids to impending disasters and presents intuitive graphical metrics to quantify the extent of topological damage. We develop two improved graph eigen-value based bounds on the grid vulnerability. Further we study adversarial attacks aimed at weakening the grid’s structural robustness and present three combinatorial algorithms to determine the optimal topological attack. Simulations on power grid networks and comparison with existing work show the improvements of the proposed measures and attack schemes.
24 POWER TRANSMISSION AND DISTRIBUTION↗
MCNP 2020: Preparing LANL Monte Carlo for Exascale Computer Systems (White Paper)
The MCNP Monte Carlo code for simulating particle transport is the product of over 60 years of Monte Carlo code development at LANL. MCNP is the most-used code worldwide for particle transport in nuclear systems, with over 10,000 users in dozens of application areas. At LANL, MCNP is used extensively in applications for nuclear criticality safety, stockpile stewardship, homeland security, radiological safety, experiment design and analysis, etc., and is considered a critical technology resource for national defense. MCNP is often referred to as the “gold standard” for radiation transport calculations, based on the long development history and extensive validation work. Today, however, this critical technology and its well-deserved reputation are at risk. We propose a targeted effort over the next several years to upgrade the core software portions of the MCNP code. This effort will focus on fundamental structural changes to the underlying MCNP algorithms, data storage, and code architecture. These changes will directly improve code maintainability, reduce the development time for new features, improve the code performance, and make the code more flexible and adaptable for expected future computer systems. To move forward with new computer systems and additional new features, it is vital to have a solid base.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Grid Data Crossing Platform (GriD-Xing)
This project is to develop a data management and analytics platform called Grid Data Crossing (GriD-Xing) that integrates data processing, storage, analysis, and visualization functionalities for users at industry (e.g., EMS and DMS) and universities. It intends to increase the applicability of large set of multi-source data (e.g., SCADA, PMU, μPMU, AMI, inverter, load/DER forecasting, and cyber data) and eventually improve the resilience, reliability, and security of critical energy infrastructure. GriD-Xing also intends to be a research platform to identify the usefulness of large multi-source data via case studies of interest to industry (e.g., 2011 southwest cold weather and 2016 Blue Cut fire events). In the first year, the team focused on the development and demonstration of large multi-source data (e.g., PMU, μPMU, SCADA, AMI, and cyber data) processing, storage, and analysis. Specifically, the team designed GriD-Xing framework in functional and structural aspects, developed GriD-Xing algorithms for large multi-source grid data processing, storage, and analysis, and finally demonstrated GriD-Xing prototype algorithms performance through a series of tests and case studies using real-world and simulation data.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Computationally Efficient Control Co-Design Optimization Framework with Mixed-Fidelity Fluid and Structure Analysis: OpenTurbineCoDe (OTCD) - ARPA-E ATLANTIS Phase I Project (Final Technical Report)
The goal of the Department of Energy (DOE) Advanced Research Projects Agency-Energy (ARPA-E) Aerodynamic Turbines Lighter and Afloat with Nautical Technologies and Integrated Servo-control (ATLANTIS) Program is to develop new technologies for floating offshore wind turbines, or wind farms, using the discipline of control co-design (CCD.) In this context, our goal is to develop a computationally efficient optimization framework for design of floating offshore wind turbines. Our specific aim is to utilize high-fidelity structural, aerodynamic, aero-structural tools, and to derive control-oriented reduced- or low-order models directly from the high-fidelity tools. We proposed a mixed-fidelity modeling approach which means that we are also using low- and mid-fidelity tools when necessary. This research is conducted by a multidisciplinary team consisting of Rutgers University, University of Michigan, Brigham Young University, and the National Renewable Energy Laboratory (NREL). The computational framework, called OpenTurbineCoDe, is designed to integrate, where possible, traditional structural, aerodynamic, aeroelastic models (e.g., OpenFAST) and advanced control algorithms with higher fidelity simulation tools including Reynolds-averaged Navier–Stokes (RANS) solvers, and three-dimensional structural finite element solvers. All the high-fidelity tools used in this research provide numerically exact gradients to facilitate both efficient optimization and local linearization for control implementation.
17 WIND ENERGY↗
ExaTN: Scalable GPU-Accelerated High-Performance Processing of General Tensor Networks at Exascale
We present ExaTN (Exascale Tensor Networks), a scalable GPU-accelerated C++ library which can express and process tensor networks on shared- as well as distributed-memory high-performance computing platforms, including those equipped with GPU accelerators. Specifically, ExaTN provides the ability to build, transform, and numerically evaluate tensor networks with arbitrary graph structures and complexity. It also provides algorithmic primitives for the optimization of tensor factors inside a given tensor network in order to find an extremum of a chosen tensor network functional, which is one of the key numerical procedures in quantum many-body theory and quantum-inspired machine learning. Numerical primitives exposed by ExaTN provide the foundation for composing rather complex tensor network algorithms. We enumerate multiple application domains which can benefit from the capabilities of our library, including condensed matter physics, quantum chemistry, quantum circuit simulations, as well as quantum and classical machine learning, for some of which we provide preliminary demonstrations and performance benchmarks just to emphasize a broad utility of our library.
97 MATHEMATICS AND COMPUTING↗
ACCESS-2: Approximation Concepts Code for Efficient Structural Synthesis, user's guide
A user's guide is presented for the ACCESS-2 computer program. ACCESS-2 is a research oriented program which implements a collection of approximation concepts to achieve excellent efficiency in structural synthesis. The finite element method is used for structural analysis and general mathematical programming algorithms are applied in the design optimization procedure.
Miura, H.↗
Computer assistance for the structural chemist
A description is presented of the approaches used to modify the molecular structure generator program, CONGEN. The CONGEN program for constructing structures under constraints has been discussed by Carhart et al. (1975). The modifications reported are to lead to a more efficient structure generation on the basis of a translation of structural data input to the program. From an algorithmic standpoint, CONGEN is successful if it can, in a reasonable amount of time and without exhausting storage resources, produce a list of candidate structures satisfying the chemist's constraints. However, this list is often quite large, and it remains for the chemist to discriminate among the candidates, eventually reducing the possibilities to just one structure. Ways are studied for providing computer assistance in examining and further constraining lists of structural candidates.
Carhart, R. E.↗
The dynamics and control of large flexible space structures
Large, flexible orbiting systems proposed for possible use in communications, electronic orbital based mail systems, and solar energy collection are discussed. The size and low weight to area ratio of such systems indicate that system flexibility is now the main consideration in the dynamics and control problem. For such large, flexible systems, both orientation and surface shape control will often be required. A conceptual development plan of a system software capability for use in analysis of the dynamics and control of large space structures technology (LSST) systems is discussed. This concept can be subdivided into four different stages: (1) system dynamics; (2) structural dynamics; (3) application of control algorithms; and (4) simulation of environmental disturbances. Modeling the system dynamics of such systems in orbit is the most fundamental component. Solar radiation pressure effects and orbital and gravity gradient effects are discussed.
Bainum, P. M.↗
Microwave emissions from snow
The radiation emitted from dry and wet snowpack in the microwave region (1 to 100 GHz) is discussed and related to ground observations. Results from theoretical model calculations match the brightness temperatures obtained by truck mounted, airborne and spaceborne microwave sensor systems. Snow wetness and internal layer structure complicate the snow parameter retrieval algorithm. Further understanding of electromagnetic interaction with snowpack may eventually provide a technique to probe the internal snow properties
Chang, A. T. C.↗