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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Symmetry reduction of tensor networks in many-body theory: I. Automated symbolic evaluation of SU(2) algebra

Abstract The ongoing progress in (nuclear) many-body theory is accompanied by an ever-rising increase in complexity of the underlying formalisms used to solve the stationary Schrödinger equation. The associated working equations at play in state-of-the-art ab initio nuclear many-body methods can be analytically reduced with respect to angular-momentum, i.e. SU (2), quantum numbers whenever they are effectively employed in a symmetry-restricted context. The corresponding procedure constitutes a tedious and error-prone but yet an integral part of the implementation of those many-body frameworks. Indeed, this symmetry reduction is a key step to advance modern simulations to higher accuracy since the use of symmetry-adapted tensors can decrease the computational complexity by orders of magnitude. While attempts have been made in the past to automate the (anti-) commutation rules linked to Fermionic and Bosonic algebras at play in the derivation of the working equations, there is no systematic account to achieve the same goal for their symmetry reduction. In this work, the first version of an automated tool performing graph-theory-based angular-momentum reduction is presented. Taking the symmetry-unrestricted expressions of a generic tensor network as an input, the code provides their angular-momentum-reduced form in an error-safe way in a matter of seconds. Several state-of-the-art many-body methods serve as examples to demonstrate the generality of the approach and to highlight the potential impact on the many-body community.

Tichai, A.↗
DOE OSTI2025

Discovering nuclear models from symbolic machine learning

Numerous phenomenological nuclear models have been proposed to describe specific observables within different regions of the nuclear chart. However, developing a unified model that describes the complex behavior of all nuclei remains an open challenge. Here, we explore whether symbolic Machine Learning (ML) can rediscover traditional nuclear physics models or identify alternatives with improved simplicity, fidelity, and predictive power. To address this challenge, we developed a Multi-objective Iterated Symbolic Regression approach that handles symbolic regressions over multiple target observables, accounts for experimental uncertainties and is robust against high-dimensional problems. As a proof of principle, we applied this method to describe the nuclear binding energies and charge radii of light and medium mass nuclei. Our approach identified simple analytical relationships based on the number of protons and neutrons, providing interpretable models with precision comparable to state-of-the-art nuclear models. Additionally, we integrated this ML-discovered model with an existing complementary model to estimate the limits of nuclear stability. These results highlight the potential of symbolic ML to develop accurate nuclear models and guide our description of complex many-body problems.

Nuclear structure↗
DOE OSTI2014

Discriminating chaotic and stochastic dynamics through the permutation spectrum test

In this paper, we propose a new heuristic symbolic tool for unveiling chaotic and stochastic dynamics: the permutation spectrum test. Several numerical examples allow us to confirm the usefulness of the introduced methodology. Indeed, we show that it is robust in situations in which other techniques fail (intermittent chaos, hyperchaotic dynamics, stochastic linear and nonlinear correlated dynamics, and deterministic non-chaotic noise-driven dynamics). We illustrate the applicability and reliability of this pragmatic method by examining real complex time series from diverse scientific fields. Taking into account that the proposed test has the advantages of being conceptually simple and computationally fast, we think that it can be of practical utility as an alternative test for determinism.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Beyond 100 Gb/s: Capacity, Flexibility, and Network Optimization

Here, we discuss building blocks that enable the exploitation of optical capacities beyond 100 Gb/s. Optical networks will benefit from more flexibility and agility in their network elements, especially from coherent transceivers. To achieve capacities of 400 Gb/s and more, coherent transceivers will operate at higher symbol rates. This will be made possible with higher bandwidth components using new electro-optic technologies implemented with indium phosphide and silicon photonics. Digital signal processing will benefit from new algorithms. Multi-dimensional modulation, of which some formats are already in existence in current flexible coherent transceivers, will provide improved tolerance to noise and fiber nonlinearities. Constellation shaping will further improve these tolerances while allowing a finer granularity in the selection of capacity. Frequency-division multiplexing will also provide improved tolerance to the nonlinear characteristics of fibers. Algorithms with reduced computation complexity will allow the implementation, at speeds, of direct pre-compensation of nonlinear propagation effects. Advancement in forward error correction will shrink the performance gap with Shannon's limit. At the network control and management level, new tools are being developed to achieve a more efficient utilization of networks. This will also allow for network virtualization, orchestration, and management. Lastly, FlexEthernet and FlexOTN will be put in place to allow network operators to optimize capacity in their optical transport networks without manual changes to the client hardware.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Profiling Production OpenSHMEM Applications, In: OpenSHMEM 2016: OpenSHMEM and Related Technologies. Enhancing OpenSHMEM for Hybrid Environments

Developing high performance OpenSHMEM applications routinely involves gaining a deeper understanding of software execution, yet there are numerous hurdles to gathering performance metrics in a production environment. Most OpenSHMEM performance profilers rely on the PSHMEM interface but PSHMEM is an optional and often unavailable feature. We present a tool that generates direct measurement performance profiles of OpenSHMEM applications even when PSHMEM is unavailable. The tool operates on dynamically linked and statically linked application binaries, does not require debugging symbols, and functions regardless of compiler optimization level. Integrated in the TAU Performance System, the tool uses automatically-generated wrapper libraries that intercept OpenSHMEM API calls to gather performance metrics with minimal overhead. Dynamically linked applications may use the tool without modifying the application binary in any way.

Computer Science↗
DOE OSTI2020

PDQ Users Manual Version 1 for PDQ Code Version 1.12

PDQ is a tool for the management of the input and execution of batch jobs for simulation codes that use a text based input system. It accomplishes this goal by operating at two levels. First, it takes input file templates (commonly known at LANL as input deck templates) and creates multiple instantiations by performing substitutions of data from table files into symbols (variables) found in the template. Second, it provides commands to submit the created files to the SLURM batch system for execution. These two activities taken together produce a whole that is greater than the sum of its parts and provides an elegant way of executing studies across multiple similar simulations while minimizing the risk of typographical errors in the input files.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

On the Goldbach Conjecture

This note proposes a confirmation proof of Goldbach’s Conjecture from 1742 [gol]: Can every even integer n > 2 be represented as the sum of 2 prime numbers? First, I present an analysis of the nature of multiplication as description length reduction for addition. This allows to explain prime numbers from first principles rather than by definition. Then, I use this analysis to derive that any natural number n must be representable by n – 1 unique two-summand additions. Given that an explicit formula for the number of primes smaller n is unknown, I formulate the contraposition to the conjecture. I then count the maximum number of possible unique additions when the summands are both non-prime or 1 and when one summand is non-prime, prime or 1 and the other is non-prime or 1. This is done using a case distinction for even and odd n. The counting is facilitated by finding the maximum number of positions a plus symbol can be placed into the unary representation of n, given the constraints. The main result is that no even n ≥ 8 exists where all n – 1 additions can be formulated without including the case of the addition of two primes. A side result is that any odd integer n can be represented by 1 addition of 2 primes if and only if n – 2 is prime.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Symbolic methods for the evaluation of sum rules of Bessel functions

The use of the umbral formalism allows a significant simplification of the derivation of sum rules involving products of special functions and polynomials. We rederive in this way known sum rules and addition theorems for Bessel functions. Furthermore, we obtain a set of new closed form sum rules involving various special polynomials and Bessel functions. The examples we consider are relevant for applications ranging from plasma physics to quantum optics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Synthesis and Structural Characterization of Ba7Li11Bi10 and AE4(Li,Tr)7Pn6 (AE = Sr, Ba, Eu; Tr = Ga, In; Pn = Sb, Bi)

Reported are the synthesis and crystal structure of Ba7Li11Bi10, a new ternary compound crystallizing in its own type with the monoclinic space group C2/m (a = 18.407(3) Å, b = 5.0258(9) Å, and c = 18.353(3) Å; β = 104.43(1)°; Pearson symbol mS56), and those of the structurally related quaternary phases Ba4(Li1−xGax)7Sb6, Ba4(Li1−xInx)7Sb6, Ba4(Li1−xInx)7Bi6, and Eu4(Li1−xInx)7Bi6 (crystallizing in the Eu4Li7Bi6 structure type with the same monoclinic space group C2/m (a = 18.4045(13)–17.642(4) Å, b = 5.012(4)–4.8297(10) Å, and c = 13.2792(10)–12.850(3) Å, β = 126.80(1)–125.85(1)°; Pearson symbol mS34). All studied compounds are identified among the products of the high-temperature reactions of the corresponding elements. Both types of crystal structures are based on corner- and edge-linked Li-centered Sb4 (or Bi4) tetrahedra, Sb6 (or Bi6) octahedra, and Sb2 or Bi2 dumbbells. Given the similarities between the two structures, it might be proposed that they represent the simplest members of a potentially large homologous series described with the general formulae (BaLi3Sb2)n(Ba3Li4Sb4)m or (BaLi3Bi2)n(Ba3Li4Bi4)m, where the more complicated “7-11-10” phase is the member with n = 2 and m = 1, while the “4-7-6” one is the intergrowth of the two components in an equal ratio. The computed electronic band structures of Ba7Li11Bi10 and idealized Ba4Li7Bi6 (a model for Ba4(Li1−xInx)7Bi6) are also discussed.

Ojwang, Dickson O. (ORCID:0000000193048975)↗
DOE OSTI2018

Software Requirements of SNAP for Editing MELCOR 2.2 Models

Applications of the severe accident analysis code MELCOR, developed for the U.S. Nuclear Regulatory Commission (NRC) by Sandia National Laboratories (SNL), have been supported by the graphical user-interface and post-processing suite Symbolic Nuclear Analysis Package (SNAP), developed for the NRC by Applied Programming Technology (APT). With the release of MELCOR 2.2, new user functionality and models have been introduced and an update to the SNAP MELCOR plugin user interface is necessary to access these new features. This document relates all new features introduced into MELCOR to the development team at APT as well as the NRC.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

From Rules to Reasoning: A Survey of Large Language Model-Based Approaches to Scientific Hypothesis and Idea Generation

Scientific hypothesis generation represents a fundamental challenge in contemporary research due to exponentially expanding literature volumes and increasing disciplinary specialization. Large language models (LLMs) have emerged as transformative tools for automated scientific discovery, moving beyond traditional rule-based and literature-mining approaches. Four paradigmatic approaches define current LLM-driven hypothesis generation: direct prompting and fine-tuning methods, knowledge-enhanced frameworks integrating retrieval-augmented generation (RAG), multi-agent collaborative systems simulating research teams, and reasoning-focused approaches implementing cognitive architectures. Domain-specific applications demonstrate statistical equivalence to human expert performance in social psychology, experimental validation in biomedical research, and near-expert quality in astronomy. Evaluation methodologies encompass human expert assessment, LLM-as-judge frameworks, and comprehensive benchmarking systems. Technical challenges include hallucination management, knowledge integration limitations, and balancing novelty with feasibility. Future directions emphasize hybrid neural-symbolic architectures and sophisticated human-AI collaboration models for responsible scientific discovery acceleration.

AI-driven discovery↗
DOE OSTI2023

Towards physics-informed explainable machine learning and causal models for materials research

From emergent material descriptions to estimation of properties stemming from structures to optimization of process parameters for achieving best performance – all key facets of materials science and related fields have experienced tremendous growth with the introduction of data-driven models. This gradual progression goes at par with developments of machine learning workflows, from purely data-driven shallow models to those that are well-capable in encoding more complex graphs, symbolic representations, invariances, and positional embeddings. Furthermore, this perspective aims at summarizing strategic aspects of such transitions while providing insights into the requirements of bringing in explainable, interpretable predictive models, and causal learning to aid in materials design and discovery. Although the focus remains on a variety of functional materials by providing a handful of case studies, the applications of such integrated methodologies are universal to facilitate fundamental understandings of materials physics while enabling autonomous experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning

Various modeling techniques are used to predict the capacity fade of Li-ion batteries. Algebraic reduced-order models, which are inherently interpretable and computationally fast, are ideal for use in battery controllers, technoeconomic models, and multi-objective optimizations. For Li-ion batteries with graphite anodes, solid-electrolyte-interphase (SEI) growth on the graphite surface dominates fade. This fade is often modeled using physically informed equations, such as square-root of time for predicting solvent-diffusion limited SEI growth, and Arrhenius and Tafel-like equations predicting the temperature and state-of-charge rate dependencies. In some cases, completely empirical relationships are proposed. However, statistical validation is rarely conducted to evaluate model optimality, and only a handful of possible models are usually investigated. This article demonstrates a novel procedure for automatically identifying reduced-order degradation models from millions of algorithmically generated equations via bi-level optimization and symbolic regression. Identified models are statistically validated using cross-validation, sensitivity analysis, and uncertainty quantification via bootstrapping. On a LiFePO 4 /Graphite cell calendar aging data set, automatically identified models utilizing square-root, power law, stretched exponential, and sigmoidal functions result in greater accuracy and lower uncertainty than models identified by human experts, and demonstrate that previously known physical relationships can be empirically "rediscovered" using machine learning.

25 ENERGY STORAGE↗
DOE OSTI2023

Combining data and theory for derivable scientific discovery with AI-Descartes

Abstract Scientists aim to discover meaningful formulae that accurately describe experimental data. Mathematical models of natural phenomena can be manually created from domain knowledge and fitted to data, or, in contrast, created automatically from large datasets with machine-learning algorithms. The problem of incorporating prior knowledge expressed as constraints on the functional form of a learned model has been studied before, while finding models that are consistent with prior knowledge expressed via general logical axioms is an open problem. We develop a method to enable principled derivations of models of natural phenomena from axiomatic knowledge and experimental data by combining logical reasoning with symbolic regression. We demonstrate these concepts for Kepler’s third law of planetary motion, Einstein’s relativistic time-dilation law, and Langmuir’s theory of adsorption. We show we can discover governing laws from few data points when logical reasoning is used to distinguish between candidate formulae having similar error on the data.

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

Giallar: push-button verification for the qiskit Quantum compiler

This paper presents Giallar, a fully-automated verification toolkit for quantum compilers. Giallar requires no manual specifications, invariants, or proofs, and can automatically verify that a compiler pass preserves the semantics of quantum circuits. To deal with unbounded loops in quantum compilers, Giallar abstracts three loop templates, whose loop invariants can be automatically inferred. To efficiently check the equivalence of arbitrary input and output circuits that have complicated matrix semantics representation, Giallar introduces a symbolic representation for quantum circuits and a set of rewrite rules for showing the equivalence of symbolic quantum circuits. With Giallar, we implemented and verified 44 (out of 56) compiler passes in 13 versions of the Qiskit compiler, the open-source quantum compiler standard, during which three bugs were detected in and confirmed by Qiskit. Furthermore, our evaluation shows that most of Qiskit compiler passes can be automatically verified in seconds and verification imposes only a modest overhead to compilation performance.

automated verification↗
DOE OSTI2017

FASTMap v. 2010.01

FASTMap is mapping application available for the web or on mobile devices (IOS and Android) that browses geospatial data and produces detailed reports of objects within any area of analysis. FASTMap can access any geospatial dataset. The software can provide immediate access to the selected data through a fully symbolized interactive mapping interface. FASTMap can load arbitrary contours that represent a region of interest and can dynamically identify and geospatially select objects that reside within the region. The software can produce a report listing the objects and aggregations for the region, as well as producing publication quality maps. FASTMap also has the ability to post and maintain authored maps, any GIS data included in the map, areas of interest, as well as any titles, and labels. These defining ingredients of a map are called map contexts. These mao contexts can be instantly broadcast via the internet through any of an infinite number of named channels to small or large numbers of users monitouring any of the channels being posted to, so a user can author a map and immediately share that map with others instantly, whether they are on traditional desktop computer, laptop, mobile tablet or smartphone. Further, users receiving broadcast maps can also alter the maps can also alter the maps, or create new ones and publish back to the channel in a collaborative manner. FASTMap can be configured to access virtually any geospatial data.

Bynum, Leo↗
DOE OSTI2021

Symbolic pregression: Discovering physical laws from distorted video

In this work, we present a method for unsupervised learning of equations of motion for objects in raw and optionally distorted unlabeled synthetic video (or, more generally, for discovering and modeling predictable features in time-series data). We first train an autoencoder that maps each video frame into a low-dimensional latent space where the laws of motion are as simple as possible, by minimizing a combination of nonlinearity, acceleration, and prediction error. Differential equations describing the motion are then discovered using Pareto-optimal symbolic regression. We find that our pre-regression (“pregression”) step is able to rediscover Cartesian coordinates of unlabeled moving objects even when the video is distorted by a generalized lens. Using intuition from multidimensional knot theory, we find that the pregression step is facilitated by first adding extra latent space dimensions to avoid topological problems during training and then removing these extra dimensions via principal component analysis. An inertial frame is autodiscovered by minimizing the combined equation complexity for multiple experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Native glycosylated HIV-1 Env in silico ensemble

The computationally modeled datasets generated in this study, namely the natively glycosylated SOSIP Env and the uniform mannose-9 glycosylated SOSIP Env ensembles will be submitted as Mendeley dataset server as required by the journal publishers, for public availability. These include two sets of Protein Data Bank (PDB)ensembles of 1000 structures each. The files are in the standardized PDB format. Each of the constituent ensemble structures are separated by the ‘END’ tag within the files. Snapshots from the beginning and end of each data set are given here. The column descriptions are as follows: Col1 –ATOM tag; Col 2 –atom number; COL 3 –atom name; COL4 –residue name; COL 5 –chain identifier; COL 6 –residue number; COL. 7,8,9 –X,Y,Z coordinates of atom in 3Dspace; COL 10 –Occupancy Factor; COL 11 -Temperature factor; COL 12 –segment identifier; COL 13 –element symbol.

59 BASIC BIOLOGICAL SCIENCES↗