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About Falyn Eisiminger

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So far Falyn Eisiminger has created 182 blog entries.

AI-Enhanced Microstructural Analysis, Simulation, and Optimization for Electrochemical Device Electrodes

  • Computer vision provides a rapid pathway from physical electrode to microstructural parameters
  • Deep neural networks provide analysis from microstructural parameters to predict long-term performance metrics  
  • Connecting the two will produce a rapid electrode assessment tool

NETL researchers have used EDX® to publicly release the largest known bank of 3D electrode microstructures of solid oxide fuel and electrolysis cells (SOCs) for training ML tools.

Access the SOC Synthetic Microstructure Bank here.

2024-09-18T17:44:19+00:00September 18th, 2024|

Subsurface Trend Analysis (STA) Method and Tool

Enhancing knowledge discovery from unstructured data using a deep learning approach to support subsurface modeling predictions

  • NETL advanced the subsurface trend analysis (STA) workflow with an AI-informed image segmentation/embedding model. 
  • The STA method was created to be a foundational technology, capable of assisting any subsurface predictive need.
  • The image embedding tool uses convolutional neural networks (CNNs) to:
    • Extract images from unstructured data
    • Categorically label the images
    • Create a repository for geologic domain postulation
  • A case study on data available for the Gulf of Mexico shows the STA image embedding tool extracts and accurately labels images with 90% to 95% precision.
  • The STA 2D Tool is available on NETL’s Energy Data eXchange® (EDX).
2024-09-18T17:24:54+00:00September 18th, 2024|

Finding High-performance Polymers for Gas Separation

Millions of polymers were screened using machine learning (ML) models ​

  • Gaussian process regression (GPR)-based ML models were developed to predict permeability and selectivity using an in-house dataset containing experimental values
  • A novel approach was developed to construct large polymer datasets
  • ~15 million polymers were screened using ML models
  • About 3,500 polymers of interest were identified for CO2/CH4 and CO2/N2 gas separation
  • ML models helped identify high-performance polymers for gas separation with the potential of transforming the field

Learn more here.

2024-09-18T17:16:35+00:00September 18th, 2024|

NETL Researchers Present at DOE’s Cybersecurity and Technology Innovation Conference

NETL experts in energy research-related artificial intelligence/machine learning (AI/ML) and the Energy Data Exchange (EDX®), which curates U.S. Department of Energy (DOE) research data, demonstrated how their work aligns with DOE’s cybersecurity and technology innovation goals at the recently concluded DOE Cybersecurity and Technology Innovation Conference (CyberCon) in Dallas, Texas.

Read More!

2024-09-20T18:31:46+00:00September 10th, 2024|

NETL Researcher Spreads the Word About Growing Quantum Sensing Expertise

NETL Research Scientist Ruishu Wright is spreading the word about the Laboratory’s growing expertise in quantum sensing — an advanced sensor technology that improves the accuracy of collecting data to assess pipeline integrity and detect gas leakage by sensing changes in motion and electric and magnetic fields on the quantum level — to national audiences.

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2024-09-05T15:47:41+00:00August 27th, 2024|

NETL Online Tool for Data Discovery Supports Development of Permits for Geologic Carbon Storage

The Carbon Storage Planning Inquiry Tool, or PlanIT, is now available on NETL’s Energy Data eXchange®, providing easy access to explore, query and evaluate thousands of relevant data features and attributes from 14 authoritative sources in one place, to support and accelerate carbon storage feasibility assessments and planning efforts.

Read More!

2024-09-05T15:49:33+00:00August 22nd, 2024|

First-of-Its-Kind Software Integration With Wafer-Scale Engine Achieved

NETL researchers recently took a significant step forward in harnessing the power of the world’s largest computer chip — the Wafer-Scale Engine (WSE) — by using an application programming interface designed in-house to connect commercial computational fluid dynamics (CFD) software through data-file sharing with the next-generation computing technology.

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2024-08-19T14:42:08+00:00August 1st, 2024|
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