Lbl/bctc Ldrd Graphical User Interface
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Pluth, AdamJ. [Idaho National Laboratory (INL), Id↗
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Results for “LDRD”
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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ldrd_virus_work
This is a Python code base that takes openly-available genetic information on known viruses and performs supervised machine learning and feature importance analysis on the relationship of the viral genomes to the competence to infect humans or bind to a specific host cell receptor.
Reddy, Tyler [LANL]↗
Shorter function summaries for finite state machine-based high consequence systems using logic synthesis and tautologies (Final Report LDRD 24-1302)
Computer programs are often viewed as collections of functions – each function has parameters (inputs) and computes a return value, and each has potential side effects that modify program state (outputs). In this research, a Sandia symbolic execution tool designed to support “human-in-the-loop” analysis was modified to automatically create “function summaries,” and a new tool, “diaboolical,” was created to support enhancing readability of the summary using a novel approach to bit-vector simplification that leverages logic synthesis and tautologies. For this effort, students at Auburn University created several finite state machines (FSMs) to serve as exemplars for high-consequence systems. Function summaries for each of the machines were obtained, and then portions of the summaries were simplified using both diaboolical and the simplification procedure of a popular SMT solver. A comparison of the results shows that diaboolical can often produce smaller function summaries, with expression length improvements over the unsimplified function summaries ranging from 0% to 90% for diaboolical and 0% to 65% for the SMT solver, though diaboolical had a significantly greater cost in time. Diaboolical was evaluated against a collection of “arbitrary” C-code as well as FSM exemplars, and for both datasets it achieved an approximately 10% improvement in expression length compared to simplifications that could be obtained using existing techniques. Function summaries can assist assurance efforts that evaluate existing systems and their executable code. A smaller function summary is likely easier for humans to understand and could thus increase the ability and efficacy of assurance practices centered around the analysis of executable artifacts.
97 MATHEMATICS AND COMPUTING↗
LLNL-LDRD-Final report
The main focus of this study is the X-ray spectroscopy and complementary modeling characterization of the range of perovskite materials. Specific areas covered in this study include: • Quantitative characterization of individual (both CB and VB) band edge movement as a function of Sn concentration; • The effects of creating an interface with the charge transport layer and back contact on both the interfacial bonding and unoccupied band structure – i.e., how does the interfacial eDOS evolve when the interface is assembled; • How does this interface change (local bonding, CB edge) evolve with degradation – this includes depth profiling; • Additional insights from the spectroscopy – e.g., the pathway of degradation (Sn(II) oxide formation to a significant depth before Sn(IV) oxide formation, etc.), do different charge transport layers have distinct properties in slowing degradation, etc.
14 SOLAR ENERGY↗
Open Call LDRD: Physically Informed Autoencoders for Galactic Redshift Regression
Physical constraints have been suggested to make neural network models more generalizable, act scientifically plausible, and be more data-efficient over unconstrained baselines. In this report, we present preliminary work on evaluating the effects of adding soft physical constraints to computer vision neural networks trained to estimate the conditional density of redshift on input galaxy images for the Sloan Digital Sky Survey. We introduce physically motivated soft constraint terms that are not implemented with differential or integral operators. We frame this work as a simple ablation study where the effect of including soft physical constraints is compared to an unconstrained baseline. We compare networks using standard point estimate metrics for photometric redshift estimation, as well as metrics to evaluate how faithful our conditional density estimate represents the probability over the ensemble of our test dataset. We find no evidence that the implemented soft physical constraints are more effective regularizers than augmentation.
97 MATHEMATICS AND COMPUTING↗
Leverage Microbial Innovations to Address Methane Emission Challenges: Input for FY24 Annual LDRD Report
Sandia researchers are addressing the urgent challenge of minimizing dilute and distributed methane emissions. The team is focused on generating stable methane-consuming microbial consortia for deployment in engineered environmental systems. This innovative work aims to produce stable inocula of these consortia and implement viral controls for microbes that generate methane, significantly reducing emissions.
54 ENVIRONMENTAL SCIENCES↗
FY24 LLNL Laboratory Directed Research and Development (LDRD) Project Accomplishments
Large duration energy storage is the key to couple renewable energy generation with power supply. This project aimed to advance the durability of a low-cost and eco-friendly flow battery technology based on iron chemistry to speed up the technology readiness level rapidly and radically. A novel device which is called “an artificial kidney” was innovated through disruptive research to integrate with the flow battery for rebalancing capacity. Key results at 50 cm2 scale demonstrates that artificial kidney enabled retaining the storage capacity of the flow battery over 100 cycles. Comparison of outcome of this work showing 0% capacity degradation to the state-of-the-art technology corroborates that this novel system is a game changing innovation.
99 GENERAL AND MISCELLANEOUS↗
Distance Learning LDRD: Using Latent Deep Learning Distances to Create Trusted Models
An overview of some results from our first year of analyses.
Wendt, Jeremy D↗
In Situ Data Analysis Through Physics-informed Tensor Decompositions (LDRD Final Report)
We introduce a new low-dimensional model of high-dimensional numerical simulation data based on low-rank tensor decompositions. Our new model aims to minimize differences between the model data and simulation data as well as functions of the model data and functions of the simulation data. This novel approach to dimensionality reduction of simulation data provides a means of directly incorporating quantities of interests and invariants associated with conservation principles associated with the simulation data into the low-dimensional model, thus enabling more accurate analysis of the simulation without requiring access to the full set of high-dimensional data. Computational results of applying this approach to two standard low-rank tensor decompositions of data arising from simulation of combustion and plasma physics are presented.
97 MATHEMATICS AND COMPUTING↗
LDRD Ending Project Review: Agile Braze Engineering: Interfacial Physics & Data-Driven Wetting Models for Heterogeneous Metal-Oxide Systems (Project #255935)
Explore the source record for details and available documents.
Chandross, Michael E.↗
Artificial Intelligence for Mission: LANL’s Director’s Initiative LDRD on AI
Explore the source record for details and available documents.
97 MATHEMATICS AND COMPUTING↗
Probabilistic Error Bounds for Low-Rank Tensor Decompositions Used in Large-Scale Data Analysis Applications (LDRD Final Report)
This report documents a research project on analyzing low-rank tensor models for data analysis that took place at Sandia National Laboratories from October 2023–September 2025. The focus of this work was to extend theoretical frameworks from statistics and probability theory for use with models for scalar, vector, and matrix data to models with tensor, or general multi-dimensional array, data. Through this work, we have provided a new set of tools for bounding errors on low-rank tensor models of both complete and sampled data. The remainder of this report is organized as follows. In Section 1, we describe the proposed work at the start of the project. Section 2 describes the research advances made as part of the project. Other research contributions in the form of conference presentations and software development is provided in Section 3. Workforce development at Sandia and Florida Atlantic University (via a subcontract on this project) is provided in Section 4.
97 MATHEMATICS AND COMPUTING↗
Radiation Effects on Network on Chips (NoC) Laboratory Directed Research and Development (LDRD) project
This project was motivated by State-of-the-Art (SOTA) technology that incorporates Network on Chips (NOC) for efficient data communication across the various computer kernels. For example, on the AMD Versal Field Programmable Gate Arrays (FPGA), an NoC has been incorporated for fast data communication from the programmable logic and other computer kernels (processing system, adaptable intelligence engines, etc.). The radiation effects on the legacy technology of this FPGA, such as the programmable logic, are well understood, and established methods exist to measure cross-sections when new families/generations are released; however, newly incorporated technologies, such as the NoC, are not fully understood and could introduce new failure points into the mission space.
36 MATERIALS SCIENCE↗
Arctic Impact Identification with Less Data Using Variable Relationships: An Exploratory Express LDRD project.
Regional impacts from sea ice loss can be challenging to separate from internal climate variability, potentially requiring thousands of ensemble members. East Asian wintertime cooling has been linked to sea ice loss from present day conditions in the Polar Amplification Model Intercomparison Project with these large ensemble counts. This cooling is theorized to arise from a strengthened Siberian High and East Asian Jet response. The strengthened Siberian High can be detected with one fifth the ensemble members needed for the East Asian wintertime cooling in a single model. We thus hypothesize that leveraging relationships between multiple variables in a conditional pathways-based approach would reduce the number of required ensemble members to conclusively attribute East Asian wintertime cooling to future sea ice concentrations. In all analyzed cases, confidence was increased when evaluating sea ice loss’s responsibility for the joint effects of East Asian cooling, East Asian Jet strengthening, and Siberian High strengthening over just East Asian cooling. However, we were not able to confidently attribute future East Asian wintertime cooling to sea ice loss in a single model. We found that significant intra-ensemble variability within single Earth System Models (ESMs) produced highly uncertain forcing response models upon which attribution results were undermined. We were able to show that ensemble mean seasonally averaged metrics from multiple ESMs greatly improved the accuracy of the forcing response linear models and exposed the necessity of all three steps in the pathway (sea ice area, Siberian High pressure, and East Asian Jet speed) for accurate prediction of East Asian wintertime cooling. Although all three steps were necessary, East Asian wintertime cooling possesses a large dependence on the Siberian High pressure, which weakens the confidence associated with overall strong joint-attribution comparing present day and future scenarios. We believe transitioning the pathway nodes to relative changes between the Siberian High and Aleutian Low as well as between the midlatitude westerlies and subtropical jet in the East Asianj Jet region may be able to produce significant attribution more fully dependent upon all three steps. Ultimately, this research demonstrates the simple extensibility of conditional pathways-based attribution to sea ice loss forcing on the Earth system.
54 ENVIRONMENTAL SCIENCES↗
LDRD Abbreviated report: High-Order General-Discrete-Ordinates Method Enabling Efficient Deterministic Transport in Hydrodynamic Simulations
Deterministic transport simulations for national-security and energy applications often operate in high-dimensional phase-space, where accuracy and cost both become major challenges. A common numerical artifact in such problems is the “ray-effect,” which appears as unphysical streaks. Beyond misinterpretation, these artifacts can contaminate tightly coupled physics, such as fluid dynamics, radiation-hydrodynamics, and laser-plasma interactions, eroding the predictive capability of entire multiphysics workflows. Our objective was to make high-dimension studies practical on modern hardware while mitigating the ray-effect without relying on prohibitively expensive sampling approaches such as Monte Carlo methods. We developed the Generic Discretization Library (GenDiL), a Graphics Processing Unit (GPU)-first framework that uses high-order Discontinuous Galerkin (DG) methods and matrix-free algorithms to reduce memory usage and improve computational efficiency, critical for phase-space simulations. GenDiL supports phase-space adaptivity in both mesh size and polynomial order (hp-adaptivity) to place resolution only where it is needed. A central capability is Local Dimensional Refinement (LDR), which couples lower-dimension continuum models to higher-dimension kinetic models through stable and conservative interfaces, so that high-fidelity physics is applied only in regions where it is essential. Building on the GenDiL framework, we developed the General SN (GSN) family of algorithms as a true generalization of the polar SN approach (discrete ordinates, often denoted SN). Rather than tying discrete ordinates to a specific polar change of coordinates, GSN formulates transport on an arbitrary change of coordinates chosen to reduce ray-effect. We studied two complementary variants: an analytic variant, where the coordinate map is prescribed in advance by a closed-form function; and a data-driven variant, where a quantity of interest, such as the net flux, guides the coordinate system. GenDiL provides the library infrastructure for efficient GPU execution, but the GSN concept is algorithmic and independent of any one library. Across representative high-dimension tests, including non-symmetric solutions, both variants delivered strong ray-effect mitigation at practical cost, moving four- to six-dimensional analysis toward repeatable, routine studies.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Materials for Energy Management across the Electromagnetic Spectrum (MEMES) (LDRD Report)
Electrification of the United States has been a major driver of economic growth, powering industrialization, modern manufacturing, and the digital economy. Today, further economic and environmental benefits can be realized by improving the energy efficiency of the technologies we have come to rely on. This project focused on enhancing the energy efficiency of two cornerstone technologies of modern society: electronic displays and building climatization. While these two technologies operate in different parts of the electromagnetic spectrum (the visible and infrared, respectively), they share a common potential technological solution: electrically-driven displays that change reflectivity. In this work, we developed the first multipixel multicolor reflective display that achieves multi-colorization with fast-changing structural color. In the course of this demonstration, we quantified the structural changes that occur across different time and length scales and resolved device sealing issues, enabling operation of these devices over months (as opposed to days previously). We furthermore developed two reflectivity changing devices in the infrared and demonstrated how these technologies can be used to reduce climatization demands for both smart window and smart wall technologies. Implementing such a device in a scaled down mock room resulted in 10 degree Celsius change.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗