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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Scaling Out a Combinatorial Algorithm for Discovering Carcinogenic Gene Combinations to Thousands of GPUs

Cancer is a leading cause of death in the US, second only to heart disease. It is primarily a result of a combination of an estimated two-nine genetic mutations (multi-hit combinations). Although a body of research has identified hundreds of cancer-causing genetic mutations, we don’t know the specific combination of mutations responsible for specific instances of cancer for most cancer types. An approximate algorithm for solving the weighted set cover problem was previously adapted to identify combinations of genes with mutations that may be responsible for individual instances of cancer. However, the algorithm’s computational requirement scales exponentially with the number of genes, making it impractical for identifying more than three-hit combinations, even after the algorithm was parallelized and scaled up to a V100 GPU. Since most cancers have been estimated to require more than three hits, we scaled out the algorithm to identify combinations of four or more hits using 1000 nodes (6000 V100 GPUs with ≈48×106 processing cores) on the Summit supercomputer at Oak Ridge National Laboratory. Efficiently scaling out the algorithm required a series of algorithmic innovations and optimizations for balancing an exponentially divergent workload across processors and for minimizing memory latency and inter-node communication. We achieved an average strong scaling efficiency of 90.14% (80.96%–97.96% for 200 to 1000 nodes), compared to a 100 node run, with 84.18% scaling efficiency for 1000 nodes. With experimental validation, the multi-hit combinations identified here could provide further insight into the etiology of different cancer subtypes and provide a rational basis for targeted combination therapy.

Dash, Sajal↗
DOE OSTI2024

Generating Euler Diagrams Through Combinatorial Optimization

Abstract Can a given set system be drawn as an Euler diagram? We present the first method that correctly decides this question for arbitrary set systems if the Euler diagram is required to represent each set with a single connected region. If the answer is yes, our method constructs an Euler diagram. If the answer is no, our method yields an Euler diagram for a simplified version of the set system, where a minimum number of set elements have been removed. Further, we integrate known wellformedness criteria for Euler diagrams as additional optimization objectives into our method. Our focus lies on the computation of a planar graph that is embedded in the plane to serve as the dual graph of the Euler diagram. Since even a basic version of this problem is known to be NP‐hard, we choose an approach based on integer linear programming (ILP), which allows us to compute optimal solutions with existing mathematical solvers. For this, we draw upon previous research on computing planar supports of hypergraphs and adapt existing ILP building blocks for contiguity‐constrained spatial unit allocation and the maximum planar subgraph problem. To generate Euler diagrams for large set systems, for which the proposed simplification through element removal becomes indispensable, we also present an efficient heuristic. We report on experiments with data from MovieDB and Twitter. Over all examples, including 850 non‐trivial instances, our exact optimization method failed only for one set system to find a solution without removing a set element. However, with the removal of only a few set elements, the Euler diagrams can be substantially improved with respect to our wellformedness criteria.

Computer Science↗
DOE OSTI2025

Combinatorial investigation of the Mn–Ge–N chemical system containing ternary nitrides Mn 3 GeN and MnGeN 2

By changing nitrogen chemical potential during synthesis of Mn–Ge–N ternary nitrides, both wurtzite MnGeN 2 and antiperovskite Mn 3 GeN ternary phases are prepared. Antiperovskite films are optically opaque and conductive, while wurtzite films with Mn/(Mn + Ge) ≤ 0.5 transmit light above ~ 2 eV. Alloys of Mn 3 GeN with Si and Al are also investigated. Mn 3 (Ge 1−x Al x )N alloys with 0.07 ≤ x ≤ 0.16 exhibit a cubic (rather than tetragonal) structure. Mn 3 (Ge 1−x Si x )N with x ≤ 0.05 maintains the tetragonal structure but becomes cubic when x > 0.05. This study shows that care must be taken in the synthesis of Mn–Ge–N and similar nitrides, especially when materials are integrated into devices not amenable to structural and chemical probing.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Constructing non-Abelian quantum spin liquids using combinatorial gauge symmetry

We construct Hamiltonians with only 1- and 2-body interactions that exhibit an exact non-Abelian gauge symmetry (specifically, combinatiorial gauge symmetry). Our spin Hamiltonian realizes the quantum double associated to the group of quaternions. It contains only ferromagnetic and anti-ferromagnetic ZZ Z Z interactions, plus longitudinal and transverse fields, and therefore is an explicit example of a spin Hamiltonian with no sign problem that realizes a non-Abelian topological phase. In addition to the spin model, we propose a superconducting quantum circuit version with the same symmetry.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Combinatorial Evaluation of Physical Feature Engineering, Classical Machine Learning, and Deep Learning Models for Synchrophasor Data at Scale

A major objective of the project was to train and evaluate the effectiveness of multiple event and anomaly detection, identification and classification deep temporal learning models for processing of real-time phasor measurement unit (PMU) data streams. A vast dataset, consisting of two years of phasor measurements from all three U.S. Interconnections, was curated and released by the Department of Energy (DOE) through Pacific Northwest National Laboratory (PNNL). The dataset also included an event log that provided event times and types (e.g. generator trips, line trips, planned service events, transformer operations, etc.). Our analysis of this dataset addressed six (6) of the eleven (11) research priorities identified in Funding Opportunity Announcement (FOA) DE-FOA-0001861 “Big Data Analysis of Synchrophasor Data” (FOA 1861). Rather than being limited to pre-determined specific algorithms, this project relied on the uniquely structured, highly performant underlying time series database capabilities of the PredictiveGrid platform to assess the vast dataset utilizing a wide variety of algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Combinatorial Membrane Synthesis: Fundamentals of Hybrid Metal-Organic Brush (MOB) Membranes for Organic Solvent Nanofiltration (Renewal)

(a) Goals: The goals of our current 3-year research project are (i) expansion of the novel modification technique Single Electron Transfer- Living Radical Polymerization (SET-LRP) to graft polymer brushes on a polyimide stable support followed by characterization and testing of the ensuing membrane (ii) stiffen brush network utilizing (a) metal organic frameworks and (b) covalent bonds formed as a result of crosslinking the grafted brush network. (b) Approach: The approach taken for this research was: (i) to graft branches terminated with a carboxyl group on each bristle (or bristle branch) allowing (a) metal immobilization to form MOF-like structures between branches or (b) crosslink bristles using aliphatic or aromatic diamines; (ii) to test the performance of the membranes using organic dyes, pure solvents (alcohols) and industrially relevant organic solvent separations; and (iii) to characterize these modified brush membranes. (c) Findings: The significant findings from the past one year are summarized below (papers resulting from this work are listed below and in D. PUBLICATIONS and relevant references are found in E. REFERENCES.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Combinatorial Glycomic Analyses to Direct CAZyme Discovery for the Tailored Degradation of Canola Meal Non-Starch Dietary Polysaccharides

Canola meal (CM), the protein-rich by-product of canola oil extraction, has shown promise as an alternative feedstuff and protein supplement in poultry diets, yet its use has been limited due to the abundance of plant cell wall fibre, specifically non-starch polysaccharides (NSP) and lignin. The addition of exogenous enzymes to promote the digestion of CM NSP in chickens has potential to increase the metabolizable energy of CM. We isolated chicken cecal bacteria from a continuous-flow mini-bioreactor system and selected for those with the ability to metabolize CM NSP. Of 100 isolates identified, Bacteroides spp. and Enterococcus spp. were the most common species with these capabilities. To identify enzymes specifically for the digestion of CM NSP, we used a combination of glycomics techniques, including enzyme-linked immunosorbent assay characterization of the plant cell wall fractions, glycosidic linkage analysis (methylation-GC-MS analysis) of CM NSP and their fractions, bacterial growth profiles using minimal media supplemented with CM NSP, and the sequencing and de novo annotation of bacterial genomes of high-efficiency CM NSP utilizing bacteria. The SACCHARIS pipeline was used to select plant cell wall active enzymes for recombinant production and characterization. This approach represents a multidisciplinary innovation platform to bioprospect endogenous CAZymes from the intestinal microbiota of herbivorous and omnivorous animals which is adaptable to a variety of applications and dietary polysaccharides.

glycome profiling↗