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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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At least 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Barren Plateaus Preclude Learning Scramblers

Scrambling processes, which rapidly spread entanglement through many-body quantum systems, are difficult to investigate using standard techniques, but are relevant to quantum chaos and thermalization. In this Letter, we ask if quantum machine learning (QML) could be used to investigate such processes. In this study we prove a no-go theorem for learning an unknown scrambling process with QML, showing that it is highly probable for any variational Ansatz to have a barren plateau landscape, i.e., cost gradients that vanish exponentially in the system size. This implies that the required resources scale exponentially even when strategies to avoid such scaling (e.g., from Ansatz -based barren plateaus or no-free-lunch theorems) are employed. Furthermore, we numerically and analytically extend our results to approximate scramblers. Hence, our work places generic limits on the learnability of unitaries when lacking prior information.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Quantum Chemistry Common Driver and Databases (QCDB) and Quantum Chemistry Engine (QCEngine): Automation and interoperability among computational chemistry programs

We report that community efforts in the computational molecular sciences (CMS) are evolving toward modular, open, and interoperable interfaces that work with existing community codes to provide more functionality and composability than could be achieved with a single program. The Quantum Chemistry Common Driver and Databases (QCDB) project provides such capability through an application programming interface (API) that facilitates interoperability across multiple quantum chemistry software packages. In tandem with the Molecular Sciences Software Institute and their Quantum Chemistry Archive ecosystem, the unique functionalities of several CMS programs are integrated, including CFOUR, GAMESS, NWChem, OpenMM, Psi4, Qcore, TeraChem, and Turbomole, to provide common computational functions, i.e., energy, gradient, and Hessian computations as well as molecular properties such as atomic charges and vibrational frequency analysis. Both standard users and power users benefit from adopting these APIs as they lower the language barrier of input styles and enable a standard layout of variables and data. These designs allow end-to-end interoperable programming of complex computations and provide best practices options by default.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Visualizing novel connections and genetic similarities across diseases using a network-medicine based approach

Understanding the genetic relationships between human disorders could lead to better treatment and prevention strategies, especially for individuals with multiple comorbidities. A common resource for studying genetic-disease relationships is the GWAS Catalog, a large and well curated repository of SNP-trait associations from various studies and populations. Some of these populations are contained within mega-biobanks such as the Million Veteran Program (MVP), which has enabled the genetic classification of several diseases in a large well-characterized and heterogeneous population. Here we aim to provide a network of the genetic relationships among diseases and to demonstrate the utility of quantifying the extent to which a given resource such as MVP has contributed to the discovery of such relations. We use a network-based approach to evaluate shared variants among thousands of traits in the GWAS Catalog repository. Our results indicate many more novel disease relationships that did not exist in early studies and demonstrate that the network can reveal clusters of diseases mechanistically related. Finally, we show novel disease connections that emerge when MVP data is included, highlighting methodology that can be used to indicate the contributions of a given biobank.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Mechanistic insights into rubidium ion adsorption at the quartz (101) surface from quantum chemical metadynamics

Ion sorption extent and mechanism depend in part on the mineral surface termination, which can be highly complex. Variations in surface functional groups, particularly with defect density and surface roughness, influence mineral reactivity towards solutes. In this work, we investigate the adsorption of a rubidium cation (Rb + ) at pristine and defect quartz (101) surface sites using well-tempered metadynamics, based on simulations with the quantum chemical density-functional tight-binding (DFTB) method. We compare the relative energetics of Rb + adsorption across selected sites for each surface, with nanosecond-level sampling, highlighting similarities between vicinal and geminal silanol sites. We find that the positive Rb + partial atomic charge can increase by as much as 0.5e as it approaches the surface, with implications for modulation of ion adsorption strength and extent at the quartz (101) surface with surface vacancies, silanol coverage, and charge.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

An Efficient Deterministic Parallel Algorithm for Adaptive Multidimensional Numerical Integration on GPUs

Recent development in Graphics Processing Units (GPUs) has enabled a new possibility for highly efficient parallel computing in science and engineering. Their massively parallel architecture makes GPUs very effective for algorithms where processing of large blocks of data can be executed in parallel. Multidimensional integration has important applications in areas like computational physics, plasma physics, computational fluid dynamics, quantum chemistry, molecular dynamics and signal processing. The computationally intensive nature of multidimensional integration requires a high-performance implementation. In this study, we present an efficient deterministic parallel algorithm for adaptive multidimensional numerical integration on GPUs. Various optimization techniques are applied to maximize the utilization of the GPU. GPU-based implementation outperforms the best known sequential methods and achieves a speed-up of up to 100. It also shows good scalability with the increase in dimensionality.

Arumugam, Kamesh↗
DOE OSTI2017

Improvement of the prediction of surface ozone concentration over conterminous U.S. by a computationally efficient second-order Rosenbrock solver in CAM4-Chem

The global chemistry-climate model (CAM4-Chem) overestimates the surface ozone concentration over the conterminous U.S. (CONUS). Reasons for this positive bias include emission, meteorology, chemical mechanism, and solver. In this study, we explore the last possibility by examining the sensitivity to the numerical methods for solving the chemistry equations. A second-order Rosenbrock (ROS-2) solver is implemented in CAM4-Chem to examine its influence on the surface ozone concentration and the computational performance of the chemistry program. Results show that under the same time step size (1800 s), statistically significant reduction of positive bias is achieved by the ROS-2 solver. The improvement is as large as 5.2 ppb in Eastern U.S. during summer season. The ROS-2 solver is shown to reduce the positive bias in Europe and Asia as well, indicating the lower surface ozone concentration over the CONUS predicted by the ROS-2 solver is not a trade-off consequence with increasing the ozone concentration at other global regions. In addition, by refining the time step size to 180 s, the first-order implicit solver does not provide statistically significant improvement of surface ozone concentration. It reveals that the better prediction from the ROS-2 solver is not only due to its accuracy but also due to its suitability for stiff chemistry equations. As an added benefit, the computation cost of the ROS-2 solver is almost half of first-order implicit solver. The improved computational efficiency of the ROS-2 solver is due to the reuse of the Jacobian matrix and lower upper (LU) factorization during its multistage calculation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Systems Life Cycle and Its Relation with the Triple Helix

This chapter examines the life cycle of complex systems in light of the dynamic interconnections among the university, industry and government sectors. Each sector is motivated in its resource allocation by principles discussed elsewhere in this book and yet remains complementary es-tablishing enduring and fundamental relationships. Industry and Government depend upon an educated workforce; universities depend upon industry to spark the R&D which is needed and to sponsor some basic research and much applied research. Government depends upon industry to address operational needs and provide finished products while universities offer government (along with industry) problem solving and problem solving environments. The life cycle of complex systems in this chapter will be examined in this context, providing historical examples. Current examples will then be examined within this multi-dimensional context with respect to the phases of program and project life cycle management from requirements definition through retirement and closeout of systems. During the explanation of these examples, the advances in research techniques to collect, analyze, and process the data will be examined.

and Law↗
DOE OSTI2023

Structural insights into opposing actions of neurosteroids on GABA A receptors

γ-Aminobutyric acid type A (GABA A ) receptors mediate fast inhibitory signaling in the brain and are targets of numerous drugs and endogenous neurosteroids. A subset of neurosteroids are GABA A receptor positive allosteric modulators; one of these, allopregnanolone, is the only drug approved specifically for treating postpartum depression. There is a consensus emerging from structural, physiological and photolabeling studies as to where positive modulators bind, but how they potentiate GABA activation remains unclear. Other neurosteroids are negative modulators of GABA A receptors, but their binding sites remain debated. Here we present structures of a synaptic GABA A receptor bound to allopregnanolone and two inhibitory sulfated neurosteroids. Allopregnanolone binds at the receptor-bilayer interface, in the consensus potentiator site. In contrast, inhibitory neurosteroids bind in the pore. MD simulations and electrophysiology support a mechanism by which allopregnanolone potentiates channel activity and suggest the dominant mechanism for sulfated neurosteroid inhibition is through pore block.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Learning continuous models for continuous physics

Abstract Dynamical systems that evolve continuously over time are ubiquitous throughout science and engineering. Machine learning (ML) provides data-driven approaches to model and predict the dynamics of such systems. A core issue with this approach is that ML models are typically trained on discrete data, using ML methodologies that are not aware of underlying continuity properties. This results in models that often do not capture any underlying continuous dynamics—either of the system of interest, or indeed of any related system. To address this challenge, we develop a convergence test based on numerical analysis theory. Our test verifies whether a model has learned a function that accurately approximates an underlying continuous dynamics. Models that fail this test fail to capture relevant dynamics, rendering them of limited utility for many scientific prediction tasks; while models that pass this test enable both better interpolation and better extrapolation in multiple ways. Our results illustrate how principled numerical analysis methods can be coupled with existing ML training/testing methodologies to validate models for science and engineering applications.

97 MATHEMATICS AND COMPUTING↗