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Wang, Cong

Publications and source records attributed to Wang, Cong.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

An agentic artificially intelligent X-ray scientist

Executing experimental tasks in both normal research laboratories and large-scale scientific facilities often requires extensive human supervision and remains a key challenge on the path to fully autonomous, artificial intelligence (AI)-driven science. Here we demonstrate a large language model-driven agent that autonomously performs X-ray sample alignment on a synchrotron beamline by planning actions, executing instrumental commands, interpreting observations and iterating towards experimental goals. Based on existing large language models with structured tool-use via the model context protocol, our AI X-ray scientist was guided and tested using an in-house-built virtual experimental setup that mirrors a six-circle diffractometer at an operational synchrotron beamline. The agentic workflow developed in the virtual environment was directly deployed on a real beamline, where it correctly identified reference reflections and determined the orientation matrix, an essential first step in any type of single-crystal scattering experiment. Our AI X-ray scientist responded effectively to unexpected experimental conditions, demonstrating adaptive problem-solving and readiness for addressing practical experimental situations. Our study provides a step towards autonomous operation across diverse experimental environments at large-scale scattering facilities.

Chen, Zhantao (ORCID:0000000319543868)↗
DOE OSTI2025

Holo-Omics disentangle drought response and biotic interactions among plant, endophyte and pathogen

Holo-omics provide a novel opportunity to study the interactions among fungi from different functional guilds in host plants in field conditions. We address the entangled responses of plant pathogenic and endophytic fungi associated with sorghum when droughted through the assembly of the most abundant fungal, endophyte genome from rhizospheric metagenomic sequences followed by a comparison of its metatranscriptome with the host plant metabolome and transcriptome. The rise in relative abundance of endophytic Acremonium persicinum (operational taxonomic unit 5 (OTU5)) in drought co-occurs with a rise in fungal membrane dynamics and plant metabolites, led by ethanolamine, a key phospholipid membrane component. The negative association between endophytic A. persicinum (OTU5) and plant pathogenic fungi co-occurs with a rise in expression of the endophyte's biosynthetic gene clusters coding for secondary compounds. Endophytic A. persicinum (OTU5) and plant pathogenic fungi are negatively associated under preflowering drought but not under postflowering drought, likely a consequence of variation in fungal fitness responses to changes in the availability of water and niche space caused by plant maturation over the growing season. Our findings suggest that the dynamic biotic interactions among host, beneficial and harmful microbiota in a changing environment can be disentangled by a blending of field observation, laboratory validation, holo-omics and ecological modelling.

Chen, Peilin↗
DOE OSTI2025

End-to-end deep learning pipeline for real-time Bragg peak segmentation: from training to large-scale deployment

X-ray crystallography reconstruction, which transforms discrete X-ray diffraction patterns into three-dimensional molecular structures, relies critically on accurate Bragg peak finding for structure determination. As X-ray free electron laser (XFEL) facilities advance toward MHz data rates (1 million images per second), traditional peak finding algorithms that require manual parameter tuning or exhaustive grid searches across multiple experiments become increasingly impractical. While deep learning approaches offer promising solutions, their deployment in high-throughput environments presents significant challenges in automated dataset labeling, model scalability, edge deployment efficiency, and distributed inference capabilities. We present an end-to-end deep learning pipeline with three key components: (1) a data engine that combines traditional algorithms with our peak matching algorithm to generate high-quality training data at scale, (2) a modular architecture that scales from a few million to hundreds of million parameters, enabling us to train large expert-level models offline while deploying smaller, distilled models at the edge, and (3) a decoupled producer-consumer architecture that separates specialized data source layer from model inference, enabling flexible deployment across diverse computing environments. Using this integrated approach, our pipeline achieves accuracy comparable to traditional methods tuned by human experts while eliminating the need for experiment-specific parameter tuning. Although current throughput requires optimization for MHz facilities, our system's scalable architecture and demonstrated model compression capabilities provide a foundation for future high-throughput XFEL deployments.

Wang, Cong↗
DOE OSTI2024

Exploring code portability solutions for HEP with a particle tracking test code

Traditionally, high energy physics (HEP) experiments have relied on x86 CPUs for the majority of their significant computing needs. As the field looks ahead to the next generation of experiments such as DUNE and the High-Luminosity LHC, the computing demands are expected to increase dramatically. To cope with this increase, it will be necessary to take advantage of all available computing resources, including GPUs from different vendors. A broad landscape of code portability tools—including compiler pragma-based approaches, abstraction libraries, and other tools—allow the same source code to run efficiently on multiple architectures. In this paper, we use a test code taken from a HEP tracking algorithm to compare the performance and experience of implementing different portability solutions. While in several cases portable implementations perform close to the reference code version, we find that the performance varies significantly depending on the details of the implementation. Achieving optimal performance is not easy, even for relatively simple applications such as the test codes considered in this work. Several factors can affect the performance, such as the choice of the memory layout, the memory pinning strategy, and the compiler used. The compilers and tools are being actively developed, so future developments may be critical for their deployment in HEP experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

FlowBench Raw Data Archive

The repo that provides data archive for DOE PoSeiDon project. It also contains scripts and instructions to parse the data.

George, Papadimitriou↗
DOE OSTI2024

Application of performance portability solutions for GPUs and many-core CPUs to track reconstruction kernels

Next generation High-Energy Physics (HEP) experiments are presented with significant computational challenges, both in terms of data volume and processing power. Using compute accelerators, such as GPUs, is one of the promising ways to provide the necessary computational power to meet the challenge. The current programming models for compute accelerators often involve using architecture-specific programming languages promoted by the hardware vendors and hence limit the set of platforms that the code can run on. Developing software with platform restrictions is especially unfeasible for HEP communities as it takes significant effort to convert typical HEP algorithms into ones that are efficient for compute accelerators. Multiple performance portability solutions have recently emerged and provide an alternative path for using compute accelerators, which allow the code to be executed on hardware from different vendors. We apply several portability solutions, such as Kokkos, SYCL, C++17 std::execution::par, Alpaka, and OpenMP/OpenACC, on two mini-apps extracted from the mkFit project: p2z and p2r. These apps include basic kernels for a Kalman filter track fit, such as propagation and update of track parameters, for detectors at a fixed z or fixed r position, respectively. The two mini-apps explore different memory layout formats. We report on the development experience with different portability solutions, as well as their performance on GPUs and many-core CPUs, measured as the throughput of the kernels from different GPU and CPU vendors such as NVIDIA, AMD and Intel.

Kwok, Ka Hei Martin↗
DOE OSTI2024

Application of performance portability solutions for GPUs and many-core CPUs to track reconstruction kernels

Next generation High-Energy Physics (HEP) experiments are presented with significant computational challenges, both in terms of data volume and processing power. Using compute accelerators, such as GPUs, is one of the promising ways to provide the necessary computational power to meet the challenge. The current programming models for compute accelerators often involve using architecture-specific programming languages promoted by the hardware vendors and hence limit the set of platforms that the code can run on. Developing software with platform restrictions is especially unfeasible for HEP communities as it takes significant effort to convert typical HEP algorithms into ones that are efficient for compute accelerators. Multiple performance portability solutions have recently emerged and provide an alternative path for using compute accelerators, which allow the code to be executed on hardware from different vendors. We apply several portability solutions, such as Kokkos, SYCL, C++17 std::execution::par and Alpaka, on two mini-apps extracted from the mkFit project: p2z and p2r. These apps include basic kernels for a Kalman filter track fit, such as propagation and update of track parameters, for detectors at a fixed z or fixed r position, respectively. The two mini-apps explore different memory layout formats. We report on the development experience with different portability solutions, as well as their performance on GPUs and many-core CPUs, measured as the throughput of the kernels from different GPU and CPU vendors such as NVIDIA, AMD and Intel.

Kwok, Ka Martin↗
DOE OSTI2023

Structural insights into functional properties of the oxidized form of cytochrome c oxidase

Abstract Cytochrome c oxidase (C c O) is an essential enzyme in mitochondrial and bacterial respiration. It catalyzes the four-electron reduction of molecular oxygen to water and harnesses the chemical energy to translocate four protons across biological membranes. The turnover of the C c O reaction involves an oxidative phase, in which the reduced enzyme (R) is oxidized to the metastable O H state, and a reductive phase, in which O H is reduced back to the R state. During each phase, two protons are translocated across the membrane. However, if O H is allowed to relax to the resting oxidized state (O), a redox equivalent to O H , its subsequent reduction to R is incapable of driving proton translocation. Here, with resonance Raman spectroscopy and serial femtosecond X-ray crystallography (SFX), we show that the heme a 3 iron and Cu B in the active site of the O state, like those in the O H state, are coordinated by a hydroxide ion and a water molecule, respectively. However, Y244, critical for the oxygen reduction chemistry, is in the neutral protonated form, which distinguishes O from O H , where Y244 is in the deprotonated tyrosinate form. These structural characteristics of O provide insights into the proton translocation mechanism of C c O.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Microwave-assisted, performance-advantaged electrification of propane dehydrogenation

Nonoxidative propane dehydrogenation (PDH) produces on-site propylene for value-added chemicals. While commercial, its modest selectivity and catalyst deactivation hamper the process efficiency and limit operation to lower temperatures. We demonstrate PDH in a microwave (MW)–heated reactor over PtSn/SiO 2 catalyst pellets loaded in a SiC monolith acting as MW susceptor and a heat distributor while ensuring comparable conditions with conventional reactors. Time-on-stream experiments show active and stable operation at 500°C without hydrogen addition. Upon increasing temperature or feed partial pressure at high space velocity, catalysts under MWs show resistance in coking and sintering, high activity, and selectivity, starkly contrasting conventional reactors whose catalyst undergoes deactivation. Mechanistic differences in coke formation are exposed. Gas-solid temperature gradients are computationally investigated, and nanoscale temperature inhomogeneities are proposed to rationalize the different performances of the heating modes. The approach highlights the great potential of electrification of endothermic catalytic reactions.

42 ENGINEERING↗
DOE OSTI2023

SpeckleNN: a unified embedding for real-time speckle pattern classification in X-ray single-particle imaging with limited labeled examples

With X-ray free-electron lasers (XFELs), it is possible to determine the three-dimensional structure of noncrystalline nanoscale particles using X-ray single-particle imaging (SPI) techniques at room temperature. Classifying SPI scattering patterns, or `speckles', to extract single-hits that are needed for real-time vetoing and three-dimensional reconstruction poses a challenge for high-data-rate facilities like the European XFEL and LCLS-II-HE. Here, we introduce SpeckleNN, a unified embedding model for real-time speckle pattern classification with limited labeled examples that can scale linearly with dataset size. Trained with twin neural networks, SpeckleNN maps speckle patterns to a unified embedding vector space, where similarity is measured by Euclidean distance. We highlight its few-shot classification capability on new never-seen samples and its robust performance despite having only tens of labels per classification category even in the presence of substantial missing detector areas. Without the need for excessive manual labeling or even a full detector image, our classification method offers a great solution for real-time high-throughput SPI experiments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

A two-stage strategy for upcycling chlorine-contaminated plastic waste

Abstract Chemical upcycling of polyolefin plastic waste to lubricant, wax and fuel-range hydrocarbons over metal-based catalysts is a crucial technological solution to the enormous environmental threat posed by plastic waste. However, currently available methods are incompatible with chlorine-contaminated feedstocks. Here we report a two-stage strategy for upcycling chlorine-contaminated polypropylene. First, magnesia–alumina mixed oxide at 30 bar H 2 and 250 °C serves as a chlorine trap by rapidly forming solid chloride, resulting in nearly complete chlorine extraction from the polyolefin melt. This enables the upcycling of plastic waste with up to 10% polyvinyl chloride content to lubricants over ruthenium-based catalysts, in the second stage. The strategy is also applicable to chlorinated aromatics and alkanes. The proposed strategy renders hydrocracking and hydrogenolysis catalysts less sensitive to the chlorine impurities in feedstocks while eliminating HCl emissions and chlorine contamination in products. It could incentivize further progress in plastics upcycling.

Environmental Sciences & Ecology↗
DOE OSTI2023

Graph neural networks for detecting anomalies in scientific workflows

Identifying and addressing anomalies in complex, distributed systems can be challenging for reliable execution of scientific workflows. We model these workflows as directed acyclic graphs (DAGs), where the nodes and edges of the DAGs represent jobs and their dependencies, respectively. We develop graph neural networks (GNNs) to learn patterns in the DAGs and to detect anomalies at the node (job) and graph (workflow) levels. We investigate workflow-specific GNN models that are trained on a particular workflow and workflow-agnostic GNN models that are trained across the workflows. Our GNN models, which incorporate both individual job features and topological information from the workflow, show improved accuracy and efficiency compared to conventional learning methods for detecting anomalies. While joint trained with multiple scientific workflows, our GNN models reached an accuracy more than 80% for workflow level and 75% for job level anomalies. In addition, we illustrate the importance of hyperparameter tuning method in our study that can significantly improve the metric(s) measure of evaluating the GNN models. Finally, we integrate explainable GNN methods to provide insights on job features in the workflow that cause an anomaly.

97 MATHEMATICS AND COMPUTING↗