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Results for “Interactive machine learning”

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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine learning inversion from small-angle scattering for charged polymers

We develop Monte Carlo simulations for uniformly charged polymers and a machine learning algorithm to interpret the intra-polymer structure factor of the charged polymer system, which can be obtained from small-angle scattering experiments. The polymer is modeled as a chain of fixed-length bonds, where the connected bonds are subject to bending energy, and there is also a screened Coulomb potential for charge interaction between all joints. The bending energy is determined by the intrinsic bending stiffness, and the charge interaction depends on the interaction strength and screening length. All three contribute to the stiffness of the polymer chain and lead to longer and larger polymer conformations. The screening length also introduces a second length scale for the polymer besides the bending persistence length. To obtain the inverse mapping from the structure factor to these polymer conformation and energy-related parameters, we generate a large data set of structure factors by running simulations for a wide range of polymer energy parameters. We use principal component analysis to investigate the intra-polymer structure factors and determine the feasibility of the inversion using the nearest neighbor distance. We employ Gaussian process regression to achieve the inverse mapping and extract the characteristic parameters of polymers from the structure factor with low relative error.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Microbial vitamin biosynthesis links gut microbiota dynamics to chemotherapy toxicity

ABSTRACT Dose-limiting toxicities pose a major barrier to cancer treatment. While preclinical studies show that the gut microbiota influences and is influenced by anticancer drugs, data from patients paired with careful side effect monitoring remains limited. Here, we investigate capecitabine (CAP)-microbiome interactions through longitudinal metagenomic sequencing of stool from 56 advanced colorectal cancer patients. CAP significantly altered the gut microbiome, enriching for menaquinol (vitamin K2) biosynthesis genes. Transposon library screens, targeted gene deletions, and media supplementation revealed that menaquinol biosynthesis protectsEscherichia colifrom drug toxicity. Stool menaquinol gene and metabolite levels were associated with decreased peripheral sensory neuropathy. Machine learning models trained in this cohort predicted toxicities in an independent cohort. Taken together, these results suggest treatment-associated increases in microbial vitamin biosynthesis serve a chemoprotective role for bacterial and host cells. Further, our findings provide a foundation for in-depth mechanistic dissection, human intervention studies, and extension to other cancer treatments. IMPORTANCE Side effects are common during the treatment of cancer. The trillions of microbes found within the human gut are sensitive to anticancer drugs, but the effects of treatment-induced shifts in gut microbes for side effects remain poorly understood. We profiled gut microbes in colorectal cancer patients treated with capecitabine and carefully monitored side effects. We observed a marked expansion in genes for producing vitamin K2 (menaquinone). Vitamin K2 rescued gut bacterial growth and was associated with decreased side effects in patients. We then used information about gut microbes to develop a predictive model of drug toxicity that was validated in an independent cohort. These results suggest that treatment-associated increases in bacterial vitamin production protect both bacteria and host cells from drug toxicity, providing new opportunities for intervention and motivating the need to better understand how dietary intake and bacterial production of micronutrients like vitamin K2 influence cancer treatment outcomes.

Microbiology↗
DOE OSTI2013

A Computational Systems Biology Study for Understanding Salt Tolerance Mechanism in Rice

Salinity is one of the most common abiotic stresses in agriculture production. Salt tolerance of rice (Oryza sativa) is an important trait controlled by various genes. The mechanism of rice salt tolerance, currently with limited understanding, is of great interest to molecular breeding in improving grain yield. In this study, a gene regulatory network of rice salt tolerance is constructed using a systems biology approach with a number of novel computational methods. We developed an improved volcano plot method in conjunction with a new machine-learning method for gene selection based on gene expression data and applied the method to choose genes related to salt tolerance in rice. The results were then assessed by quantitative trait loci (QTL), co-expression and regulatory binding motif analysis. The selected genes were constructed into a number of network modules based on predicted protein interactions including modules of phosphorylation activity, ubiquity activity, and several proteinase activities such as peroxidase, aspartic proteinase, glucosyltransferase, and flavonol synthase. All of these discovered modules are related to the salt tolerance mechanism of signal transduction, ion pump, abscisic acid mediation, reactive oxygen species scavenging and ion sequestration. We also predicted the three-dimensional structures of some crucial proteins related to the salt tolerance QTL for understanding the roles of these proteins in the network. Our computational study sheds some new light on the mechanism of salt tolerance and provides a systems biology pipeline for studying plant traits in general.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Discovery of multi-functional polyimides through high-throughput screening using explainable machine learning

Polyimides have been widely used in modern industries because of their excellent mechanical and thermal properties, e.g., high-temperature fuel cells, displays, and aerospace composites. However, it usually takes decades of experimental efforts to develop a successful product. Aiming to expedite the discovery of high-performance polyimides, we utilize computational methods of machine learning (ML) and molecular dynamics (MD) simulations. Our study provides compelling evidence for the effectiveness of a data-driven approach in discovering novel polyimides. We first build a comprehensive library of more than 8 million hypothetical polyimides based on the polycondensation of existing dianhydride and diamine/diisocyanate molecules. Then we establish multiple ML models for the thermal and mechanical properties of polyimides based on their experimentally reported values, including glass transition temperature, Young’s modulus, and tensile yield strength. The obtained ML models demonstrate excellent predictive performance in identifying the key chemical substructures influencing the thermal and mechanical properties of polyimides. The use of explainable machine learning describes the effect of chemical substructures on individual properties, from which human experts can understand the cause of the ML model decision. Applying the well-trained ML models, we obtain property predictions of the 8 million hypothetical polyimides. Then, we screen the whole hypothetical dataset and identify three (3) best-performing novel polyimides that have better-combined properties than existing ones through Pareto frontier analysis. For an easy query of the discovered high-performing polyimides, we also create an online platform https://polyimide-explorer.herokuapp.com/ that embeds the developed ML model with interactive visualization. Furthermore, we validate the ML predictions through all-atom MD simulations and examine their synthesizability. The MD simulations are in good agreement with the ML predictions and the three novel polyimides are predicted to be easy to synthesize via Schuffenhauer’s synthetic accessibility score. Following the proposed ML guidance, we successfully synthesized a novel polyimide and the experimentally obtained high glass transition/thermal decomposition temperature demonstrated its excellent thermal stability. Here our study demonstrates an efficient way to expedite the discovery of novel polymers using ML prediction and MD validation. The high-throughput screening of a large computational dataset can serve as a general approach for new material discovery in other polymeric material exploration problems, such as organic photovoltaics, polymer membranes, and dielectrics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A structured framework for predicting sustainable aviation fuel properties using liquid-phase FTIR and machine learning

Sustainable aviation fuels have the potential to improve efficiency, reduce emissions, and enhance energy security. To help identify viable sustainable aviation fuels and accelerate research, machine learning models have been developed to predict relevant physicochemical properties. However, many models have limited applicability, leverage data from complex analytical techniques with confined spectral ranges, or use feature decomposition methods that offer limited interpretability. Using liquid-phase Fourier Transform Infrared (FTIR) spectra, this study presents a structured method for creating accurate and interpretable property prediction models for neat molecules, aviation fuels, and blends. Liquid FTIR spectra can be collected quickly and consistently, offering high reliability, sensitivity, and component specificity using less than 2 ml of sample. The method first decomposes FTIR spectra into fundamental building blocks using non-negative matrix factorization (NMF) to enable scientific analysis of FTIR spectra attributes and fuel properties. The NMF features are then used to create five ensemble models for predicting final boiling point, flash point, freezing point, density at 15°C, and kinematic viscosity at -20°C. All models were trained using experimental property data from neat molecules, aviation fuels, and blends. The models accurately predict key properties across a broad range of neat molecules and representative fuels and blends, while enabling interpretation of relationships between compositional elements, such as functional groups or chemical classes, and their resulting properties. This demonstrates strong potential to support sustainable aviation fuel research and development. The models and data are available on an interactive web tool.

Fourier transform infrared spectroscopy↗
DOE OSTI2025

deadtrees.earth — An open-access and interactive database for centimeter-scale aerial imagery to uncover global tree mortality dynamics

Excessive tree mortality is a global concern and remains poorly understood as it is a complex phenomenon. We lack global and temporally continuous coverage on tree mortality data. Ground-based observations on tree mortality, e.g., derived from national inventories, are very sparse, and may not be standardized or spatially explicit. Earth observation data, combined with supervised machine learning, offer a promising approach to map overstory tree mortality in a consistent manner over space and time. However, global-scale machine learning requires broad training data covering a wide range of environmental settings and forest types. Low altitude observation platforms (e.g., drones or airplanes) provide a cost-effective source of training data by capturing high-resolution orthophotos of overstory tree mortality events at centimeter-scale resolution. Here, we introduce deadtrees.earth, an open-access platform hosting more than two thousand centimeter-resolution orthophotos, covering more than 1,000,000 ha, of which more than 58,000 ha are manually annotated with live/dead tree classifications. This community-sourced and rigorously curated dataset can serve as a comprehensive reference dataset to uncover tree mortality patterns from local to global scales using space-based Earth observation data and machine learning models. This will provide the basis to attribute tree mortality patterns to environmental changes or project tree mortality dynamics to the future. The open nature of deadtrees.earth, together with its curation of high-quality, spatially representative, and ecologically diverse data will continuously increase our capacity to uncover and understand tree mortality dynamics.

Citizen science↗
DOE OSTI2022

BASIN-3D: A brokering framework to integrate diverse environmental data

Diverse observational and simulation datasets are needed to understand and predict complex ecosystem behavior over seasonal to decadal and century time-scales. Integration of these datasets poses a major barrier towards advancing environmental science, particularly due to differences in the structure and formats of data provided by various sources. Here, we describe BASIN-3D (Broker for Assimilation, Synthesis and Integration of eNvironmental Diverse, Distributed Datasets), a data integration framework designed to dynamically retrieve and transform heterogeneous data from different sources into a common format to provide an integrated view. BASIN-3D enables users to adopt a standardized approach for data retrieval and avoid customizations for the data type or source. We demonstrate the value of BASIN-3D with two use cases that require integration of data from regional to watershed spatial scales. The first application uses the BASIN-3D Python library to integrate time-series hydrological and meteorological data to provide standardized inputs to analytical and machine learning codes in order to predict the impacts of hydrological disturbances on large river corridors of the United States. The second application uses the BASIN-3D Django framework to integrate diverse time-series data in a mountainous watershed in East River, Colorado, United States to enable scientific researchers to explore and download data through an interactive web portal. Thus, BASIN-3D can be used to support data integration for both web-based tools, as well as data analytics using Python scripting and extensions like Jupyter notebooks. The framework is expected to be transferable to and useful for many other field and modeling studies.

Varadharajan, C↗
DOE OSTI2023

Machine-learning-aided density functional theory calculations of stacking fault energies in steel

A combined large-scale first principles approach with machine learning and materials informatics is proposed to quickly sweep the chemistry-composition space of advanced high strength steels (AHSS). AHSS are composed of iron and key alloying elements such as aluminum and manganese. A systematic exploration of the distribution of aluminum and manganese atoms in iron is used to investigate low stacking fault energies configurations using first principles calculations. To overcome the computational cost of exploring the composition space, this process is sped up using an automated machine learning tool: DeepHyper. Here our results predict that it is energetically favorable for Al to stay away from a stacking fault, but Mn atoms do not affect the stacking fault energy and can stay in the vicinity of the fault. The distribution of Al and Mn atoms in systems containing stacking faults and the effects of their interactions on the equilibrium distribution are systematically analyzed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

DancePartner: Python Package to Mine Multiomics Relationship Networks from Literature and Databases

A goal of multi-omics experiments is to understand how mechanistic molecular biology is altered between conditions, typically a control group and experimental groups. Oftentimes this involves studying changes in biomolecule relationships (e.g. interactions, metabolic relationships) of several types of biomolecules (e.g. proteins, lipids, metabolites). Though several databases contain relationships between biomolecules, understudied species may have little to no relationship information in databases and thus must be mined from literature. There are several challenges to literature mining, including automated full-text extraction, duplicate biomolecule term collapsing, and implementing complex machine learning tools. To make relationship extraction more accessible to the community, a python package called DancePartner was developed to allow for the extraction of relationships from literature and databases, with functions to map biomolecule synonyms to standardized identifiers and visualize and characterize the resulting multi-omics network. Here, in this study, an example dataset involving Caenorhabditis elegans is presented, where relationships are mined from 1443 publications using DancePartner. These relationships are combined with relationships from KEGG, WikiPathways, UniProt, and LipidMaps, and visualized.

BERT↗
DOE OSTI2024

Self-Supervised Cloud Classification

Abstract Low-level marine clouds play a pivotal role in Earth’s weather and climate through their interactions with radiation, heat and moisture transport, and the hydrological cycle. These interactions depend on a range of dynamical and microphysical processes that result in a broad diversity of cloud types and spatial structures, and a comprehensive understanding of cloud morphology is critical for continued improvement of our atmospheric modeling and prediction capabilities moving forward. Deep learning has recently accelerated our ability to study clouds using satellite remote sensing, and machine learning classifiers have enabled detailed studies of cloud morphology. A major limitation of deep learning approaches to this problem, however, is the large number of hand-labeled samples that are required for training. This work applies a recently developed self-supervised learning scheme to train a deep convolutional neural network (CNN) to map marine cloud imagery to vector embeddings that capture information about mesoscale cloud morphology and can be used for satellite image classification. The model is evaluated against existing cloud classification datasets and several use cases are demonstrated, including training cloud classifiers with very few labeled samples, interrogation of the CNN’s learned internal feature representations, cross-instrument application, and resilience against sensor calibration drift and changing scene brightness. The self-supervised approach learns meaningful internal representations of cloud structures and achieves comparable classification accuracy to supervised deep learning methods without the expense of creating large hand-annotated training datasets. Significance Statement Marine clouds heavily influence Earth’s weather and climate, and improved understanding of marine clouds is required to improve our atmospheric modeling capabilities and physical understanding of the atmosphere. Recently, deep learning has emerged as a powerful research tool that can be used to identify and study specific marine cloud types in the vast number of images collected by Earth-observing satellites. While powerful, these approaches require hand-labeling of training data, which is prohibitively time intensive. This study evaluates a recently developed self-supervised deep learning method that does not require human-labeled training data for processing images of clouds. We show that the trained algorithm performs competitively with algorithms trained on hand-labeled data for image classification tasks. We also discuss potential downstream uses and demonstrate some exciting features of the approach including application to multiple satellite instruments, resilience against changing image brightness, and its learned internal representations of cloud types. The self-supervised technique removes one of the major hurdles for applying deep learning to very large atmospheric datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Plant Single-Cell Solutions for Energy and the Environment (Second Workshop Report)

Plants are important sources of energy and materials, and they collectively represent a critical component of Earth’s ecosystem. With increasing environmental stresses due to climate change and intensive agricultural practices, the need for resilient plants is greater than ever before. To secure plant resources for bioenergy, biomaterials, food, and ecosystem adaptation, a deeper understanding of the fundamental biology of plants at a cellular level is urgently needed. Plants contain a multitude of specialized cell types that compose tissues and organs. Pathogens often target specific cell types within plants, and the response of one cell to a particular stimulus is likely to be distinct from its neighbor because of underlying molecular and contextual differences. Understanding how these responses are distributed among cells, the main goal of single-cell approaches, will substantially enhance our ability to use targeted engineering for improving plant productivity and resilience. Furthermore, single-cell approaches are necessary to understand the interactions between plants and other ecosystem members such as fungi, bacteria, and archaea. Unlocking these gene-response mechanisms at a cellular level can improve our ability to adapt plants to environmental stresses, increasing their utility as feedstocks for biomaterials and bioenergy. Recent advances in high-throughput sequencing, mass spectrometry, microfluidics and miniaturization, artificial intelligence and machine learning, and bioinformatics have greatly improved our ability to detect and understand processes at a cellular level. In mammalian systems, single-cell transcriptomics has already led to many advances, such as newly identified cell types and cell-targeted treatment of diseases, and mass spectrometry-based single-cell proteomics has recently been demonstrated as a promising emerging technology. However, plant single-cell omics has lagged behind mammalian approaches due to the high cost of the technologies relative to available resources and to the innate biological features of plants, including the complexity of the cell wall and polyploidy. To better understand how single-cell methods could enable plant science, Lawrence Berkeley National Laboratory (Berkeley Lab) hosted a workshop on April 29, 2021, that brought together a diverse group of leaders in plant and/or single-cell biology. Attendees represented federal research programs and domestic and international academic institutions. During the workshop, three presenters described the current state of research in both experimental and computational approaches. While the focus of the workshop was on factors preventing plant biology researchers from fully adopting single-cell methodologies, workshop participants agreed that most barriers could be overcome with focused, strategic investment and coordinated efforts among institutions leading to significant scientific discoveries that would be difficult to obtain using more conventional technologies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Genomic fingerprints of the world’s soil ecosystems

Despite the explosion of soil metagenomic data, we lack a synthesized understanding of patterns in the distribution and functions of soil microorganisms. These patterns are critical to predictions of soil microbiome responses to climate change and resulting feedbacks that regulate greenhouse gas release from soils. To address this gap, we assay 1,512 manually curated soil metagenomes using complementary annotation databases, read-based taxonomy, and machine learning to extract multidimensional genomic fingerprints of global soil microbiomes. Our objective is to uncover novel biogeographical patterns of soil microbiomes across environmental factors and ecological biomes with high molecular resolution. We reveal shifts in the potential for (i) microbial nutrient acquisition across pH gradients; (ii) stress-, transport-, and redox-based processes across changes in soil bulk density; and (iii) greenhouse gas emissions across biomes. We also use an unsupervised approach to reveal a collection of soils with distinct genomic signatures, characterized by coordinated changes in soil organic carbon, nitrogen, and cation exchange capacity and in bulk density and clay content that may ultimately reflect soil environments with high microbial activity. Genomic fingerprints for these soils highlight the importance of resource scavenging, plant-microbe interactions, fungi, and heterotrophic metabolisms. Across all analyses, we observed phylogenetic coherence in soil microbiomes—more closely related microorganisms tended to move congruently in response to soil factors. Collectively, the genomic fingerprints uncovered here present a basis for global patterns in the microbial mechanisms underlying soil biogeochemistry and help beget tractable microbial reaction networks for incorporation into process-based models of soil carbon and nutrient cycling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Machine learning enhanced characterization and optimization of photonic cured MAPbI 3 for efficient perovskite solar cells

Photonic curing (PC) can facilitate high-speed perovskite solar cell (PSC) manufacturing because it uses high-intensity light pulses to crystallize perovskite films in milliseconds. However, optimizing PC conditions is challenging due to its many variables, and using power conversion efficiency (PCE) as the optimization metric is both time-consuming and labor-intensive. This work presents a machine learning (ML) approach to optimize PC conditions for fabricating methylammonium lead iodide (MAPbI 3 ) films by quantitatively comparing their ultraviolet-visible (UV-vis) absorbance spectra to thermal annealed (TA) films using four similarity metrics. We perform Bayesian optimization coupled with Gaussian process regression (BO-GP) to minimize the similarity metrics. Refining PC conditions using active learning based on BO-GP models, we achieve a PC MAPbI3 film with an absorbance spectrum closely matching a TA reference film, which is further verified by its crystalline and morphological properties. Thus, we demonstrate that the UV-vis absorption spectrum can accurately proxy film quality. Additionally, we use an AI-based segmentation model for a more efficient grain size analysis. However, when we use the optimized PC condition to fabricate PSCs, we find that interaction between MAPbI 3 and the hole transport layer (HTL) during PC critically degrades the PSC performance. By adding a buffer layer between the HTL and MAPbI 3 , the optimized PC PSCs produce a champion PCE of 11.8%, comparable to the TA reference of 11.7%. Using UV-vis similarity metrics instead of device PCE as the objective in our BO-GP method accelerates the optimization of PC processing conditions for MAPbI 3 films.

14 SOLAR ENERGY↗
DOE OSTI2022

Complex Oxides for Brain–Inspired Computing: A Review

The fields of brain-inspired computing, robotics, and, more broadly, artificial intelligence (AI) seek to implement knowledge gleaned from the natural world into human-designed electronics and machines. In this review, the opportunities presented by complex oxides, a class of electronic ceramic materials whose properties can be elegantly tuned by doping, electron interactions, and a variety of external stimuli near room temperature, are discussed. The review begins with a discussion of natural intelligence at the elementary level in the nervous system, followed by collective intelligence and learning at the animal colony level mediated by social interactions. An important aspect highlighted is the vast spatial and temporal scales involved in learning and memory. The focus then turns to collective phenomena, such as metal-to-insulator transitions (MITs), ferroelectricity, and related examples, to highlight recent demonstrations of artificial neurons, synapses, and circuits and their learning. First-principles theoretical treatments of the electronic structure, and in situ synchrotron spectroscopy of operating devices are then discussed. The implementation of the experimental characteristics into neural networks and algorithm design is then revewed. Finally, outstanding materials challenges that require a microscopic understanding of the physical mechanisms, which will be essential for advancing the frontiers of neuromorphic computing, are highlighted.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Data-Driven Discovery and Experimental Validation of Solvent Polarity Effects on Conjugated Polymer Solution-to-Film Assembly Pathways

Understanding how solvent properties influence the solution-to-film assembly of conjugated polymers remains a critical challenge due to the complex and intertwined nature of polymer–solvent interactions. In this study, we integrate a data-driven framework with experimental validation to identify key parameters influencing the assembly and performance of poly[2,5-(2-octyldodecyl)-3,6-diketopyrrolopyrrole-alt-5,5-(2,5-di(thien-2-yl)thieno[3,2-b]thiophene)] (DPP-DTT) in organic field-effect transistors (OFETs). A machine learning (ML) approach identified the normalized Reichardt polarity parameter (E T N ) as a significant descriptor correlated with DPP-DTT hole mobility (μ). Systematic DPP-DTT devices fabricated using solvents across a wide E T N range revealed that higher E T N solvents yield enhanced μ. To elucidate the structural origins of high μ, we conducted comprehensive analyses using UV–vis–NIR spectroscopy and grazing incidence wide angle X-ray scattering (GIWAXS) measurements. The results revealed that films processed from high E T N solvents exhibit reduced paracrystallinity. By analyzing the solution-state behavior using optical microscopy and solution WAXS, we revealed polymer solubility differences in the various solvents and associated distinct polymer assembly pathways, elucidating why the high E T N solvent produces long-range ordered films. Notably, the high E T N solvent shows a pronounced preference for liquid-crystal (LC)-mediated assembly, providing a mechanistic explanation for the enhanced structural order. Therefore, these results demonstrate that solvent polarity, as evaluated by E T N , serves as an important parameter that plays a significant role in the DPP-DTT assembly pathway and resultant solid-state morphology. This work provides a strategy for integrating data science with experiments to identify critical parameters associated with complex polymer systems and helps guide rational process design for high-performance organic electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A controlled-NOT gate for frequency-bin qubits

The realization of strong photon–photon interactions has presented an enduring challenge across photonics, particularly in quantum computing, where two-photon gates form essential components for scalable quantum information processing (QIP). While linear-optic schemes have enabled probabilistic entangling gates in spatio-polarization encoding, solutions for many other useful degrees of freedom remain missing. In particular, no two-photon gate for the important platform of frequency encoding has been experimentally demonstrated, due in large part to the additional challenges imparted by the mismatched wavelengths of the interacting photons. In this article, we design and implement an entangling gate for frequency-bin qubits, a coincidence-basis controlled-NOT (CNOT), using line-by-line pulse shaping and electro-optic modulation. With this being said, we extract a quantum unitary fidelity of 0.91 ± 0.01 via a parameter inference approach based on Bayesian machine learning, which enables accurate gate reconstruction from measurements in the two-photon computational basis alone. Thus, our CNOT imparts a single-photon frequency shift controlled by the frequency of another photon—an important capability in itself—and should enable new directions in fiber-compatible QIP.

42 ENGINEERING↗
DOE OSTI2025

CGSim: A Simulation Framework for Large Scale Distributed Computing Environment

Large-scale distributed computing infrastructures such as the Worldwide LHC Computing Grid (WLCG) require comprehensive simulation tools for evaluating performance, testing new algorithms, and optimizing resource allocation strategies. However, existing simulators suffer from limited scalability, hardwired algorithms, lack of real-time monitoring, and inability to generate datasets suitable for modern machine learning approaches. We present CGSim, a simulation framework for large-scale distributed computing environments that addresses these limitations. Built upon the validated SimGrid simulation framework, CGSim provides high-level abstractions for modeling heterogeneous grid environments while maintaining accuracy and scalability. Key features include a modular plugin mechanism for testing custom workflow scheduling and data movement policies, interactive real-time visualization dashboards, and automatic generation of event-level datasets suitable for AI-assisted performance modeling. We demonstrate CGSim’s capabilities through a comprehensive evaluation using production ATLAS PanDA workloads, showing significant calibration accuracy improvements across WLCG computing sites. Scalability experiments show near-linear scaling for multi-site simulations, with distributed workloads achieving 6 × better performance compared to single-site execution. The framework enables researchers to simulate WLCG-scale infrastructures with hundreds of sites and thousands of concurrent jobs within practical time budget constraints on commodity hardware.

Vatsavai, Sairam Sri [Brookhaven National Laborato↗
DOE OSTI2021

Electricity Market Dynamics as Smooth Games: Organizing Gradient-Based Learners and Identifying Instabilities

Wide-spread adoption of machine learning and distributed energy production has the potential to fundamentally change behaviours of energy markets. In practice, along with the benefits, previously unseen instabilities have begun emerging in markets currently embracing these technologies. In this report we study electricity markets as dynamical systems arising in smooth games, where energy consumers are modelled as gradient-based learners who interact on a graph through distributed energy producers. We give simple examples of how instabilities can arise even when the consumers’ local objectives seem reasonable and how these instabilities can be remedied. Drawing insights from these examples, we begin developing a general model of optimization-driven electricity market dynamics and give conditions for ensuring local Nash equilibria of the system are stable fixed points of the dynamics. Our results demonstrate how models of electricity market dynamics might be used for deriving formal guarantees in order to stabilize markets and enact regulatory policies in the face of market shocks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗