More results...

Filter by Categories
Publications
Presentations

Computational Discovery of Fast Interstitial Oxygen Conductors

Meng, J., Sheikh, M.S., Jacobs, R., Liu, J., Nachlas, W.O., Li, X., and Morgan, D., 2024, Computational Discovery of Fast Interstitial Oxygen Conductors. Nature Materials. https://doi.org/10.1038/s41563-024-01919-8

Aging heat treatment design for Haynes 282 made by wire-feed additive manufacturing using high-throughput experiments and interpretable machine learning

Want, X., Pizano, L.F.P., Sridar, S., Sudbrack, C., and Xiong, W., 2024, Aging heat treatment design for Haynes 282 made by wire-feed additive manufacturing using high-throughput experiments and interpretable machine learning. Science and Technology of Advanced Materials, 25(1). https://doi.org/10.1080/14686996.2024.2346067

Advanced Offshore Hazard Forecasting to Enable Resilient Offshore Operations

Mark-Moser, M., Romeo, L., Duran, R., Bauer, J. R., and K. Rose. April 29, 2024. “Advanced Offshore Hazard Forecasting to Enable Resilient Offshore Operations” [Conference Paper]. Offshore Technology Conference 2024, Houston, Texas. https://doi.org/10.4043/35221-MS

Machine Learning Discrimination and Ultrasensitive Detection of Fentanyl Using Gold Nanoparticle-Decorated Carbon Nanotube-Based Field-Effect Transistor Sensors

Shao, W., Sorescu, D.C., Liu, Z., Star, A., 2024, Machine Learning Discrimination and Ultrasensitive Detection of Fentanyl Using Gold Nanoparticle-Decorated Carbon Nanotube-Based Field-Effect Transistor Sensors. Small, 2311835. https://doi.org/10.1002/smll.202311835

Lab Scale Demonstration of Pipeline Third-Party Damage Classification Using Convolutional Neural Networks

Bukka, S. R.; Lalam, N.; Bhatta, H.; Wright, R. “Lab Scale Demonstration of Pipeline Third-Party Damage Classification Using Convolutional Neural Networks” [Conference Paper], SPIE Defense + Commercial Sensing, National Harbor, MD, April 24, 2024.

Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits

Liu, G., Wu, X., and Romanov, V., 2024, Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits. Applied Sciences 14(7), 2927. https://doi.org/10.3390/app14072927

Multi-Parametric Gas Sensing for Transformer Monitoring Using an Optical Fiber Sensor Array

Wuenschell, J., Kim, K.J., Lander, G., and Buric, M., (2023). Multi-Parametric Gas Sensing for Transformer Monitoring Using an Optical Fiber Sensor Array. Proc. SPIE 12532, Optical Waveguide and Laser Sensors II. https://doi.org/10.1117/12.2663804

The Kimberlina synthetic multiphysics dataset for CO2 monitoring investigations

Alumbaugh, D., Gasperikova, E., Crandall, D., Commer, M., Feng, S., Harbert, W., Li, Y., Lin, Y., and Samarasinghe, S., (2023). The Kimberlina synthetic multiphysics dataset for CO2 monitoring investigations. Geoscience Data Journal. https://doi.org/10.1002/gdj3.191

Wave Detection and Tracking Within a Rotating Detonation Engine Through Object Detection

Johnson, K.B., Ferguson, D.H., Nix, A.C., and Tallman, Z., (2023). Wave Detection and Tracking Within a Rotating Detonation Engine Through Object Detection. Journal of Propulsion and Power, 39(4). https://doi.org/10.2514/1.B38960

Convoluted Filtering for Process Cycle Modeling

Romanov, V. (2023). Convoluted Filtering for Process Cycle Modeling. Engineering Reports, 5(11), e12657. https://doi.org/10.1002/eng2.12657

Data-driven discovery of a formation prediction rule on high-entropy ceramics

Yan, Y., Pei, Z., Gao, M.C., Misture, S., Wang, K., (2023). Data-driven discovery of a formation prediction rule on high-entropy ceramics. Acta Materialia, 253, 118955, https://doi.org/10.1016/j.actamat.2023.118955

Application of unsupervised deep learning to image segmentation and in-situ contact angle measurements in a CO2-water-rock system

Wang, H., Dalton, L., Guo, R., McClure, J., Crandall, D., & Chen, C., (2023). Application of unsupervised deep learning to image segmentation and in-situ contact angle measurements in a CO2-water-rock system. Advances in Water Resources, 173(104385). https://doi.org/10.1016/j.advwatres.2023.104385

Development of an equation-based parallelization method for multiphase particle-in-cell simulations

Woo, M., Jordan, T., Nandi, T., Dietiker, J.F., Guenther, C., and Van Essendelft, D., (2022). Development of an equation-based parallelization method for multiphase particle-in-cell simulations. Engineering with Computers. https://doi.org/10.1007/s00366-022-01768-6

Disruptive Changes in Field Equation Modeling: A Simple Interface for Wafer Scale Engines

Woo, M., Jordan, T., Schreiber, R., Sharapov, I., Muhammad, S., Koneru, A., James, M., & Van Essendelft, D. (2022). Disruptive Changes in Field Equation Modeling: A Simple Interface for Wafer Scale Engines. arXiv. https://doi.org/10.48550/arxiv.2209.13768

Data-driven offshore CO2 saline storage assessment methodology

Romeo, L., Thomas, R., Mark-Moster, M., Bean, A., Bauer, J., & Rose, K., (2022). Data-driven offshore CO2 saline storage assessment methodology. International Journal of Greenhouse Gas Control, 119. https://doi.org/10.1016/j.ijggc.2022.103736

High performance finite element simulations of infiltrated solid oxide fuel cell cathode microstructures

Hsu, T., Kim, H., Mason, J.H., Mahbub, R., Epting, W.K., Abernathy, H.W., Hackett, G.A., Litster, S., Rollett, A.D., & Salvador, P.A. (2022). High performance finite element simulations of infiltrated solid oxide fuel cell cathode microstructures. Journal of Power Sources, 541, https://doi.org/10.1016/j.jpowsour.2022.231652

A Multi-criteria CCUS Screening Evaluation of the Gulf of Mexico, USA

Wendt, A., Sheriff, A., Shih, C.Y., Vikara, D., & Grant, T. (2022). A Multi-criteria CCUS Screening Evaluation of the Gulf of Mexico, USA. International Journal of Greenhouse Gas Control, 118. https://doi.org/10.1016/j.ijggc.2022.103688

Assessment of Outliers in Alloy Datasets Using Unsupervised Techniques

Wenzlick, M., Mamun, O., Devanathan, R., Rose, K., & Hawk, J. (2022). Assessment of Outliers in Alloy Datasets Using Unsupervised Techniques. JOM, 74, 2846-2859. https://doi.org/10.1007/s11837-022-05204-4

Latent Learning with pyroMind.2020

Romanov, V., (2021). Latent Learning with pyroMind.2020. 2021 IEE International Conference on Big Data, pp. 4624-4627, https://doi.org/10.1109/BigData52589.2021.9671643

Machine learning accelerated discrete element modeling of granular flows

Lu, L., Gao, X., Dietiker, J.F., Shahnam, M., & Rogers, W.A. (2021). Machine learning accelerated discrete element modeling of granular flows. Chemical Engineering Science, 245. https://doi.org/10.1016/j.ces.2021.116832

Machine learning approach to transform scattering parameters to complex permittivities

Tempke, R., Thomas, L., Wildefire, C., Shekhawat, D., & Musho, T., (2021). Machine learning approach to transform scattering parameters to complex permittivities. Journal of Microwave Power and Electromagnetic Energy, 55(4), 287-302, https://doi.org/10.1080/08327823.2021.1993046

Machine-Learning Microstructure for Inverse Material Design

Pei, Z., Rozman, K.A., Dogan, O.N., Wen, Y., Gao, N., Holm, E.A., Hawk, J.A., Alman, D.E., & Gao, M.C., (2021). Machine-Learning Microstructure for Inverse Material Design. Advanced Science, 8(23). https://doi.org/10.1002/advs.202101207

Neural network-based order parameter for phase transitions and its applications in high-entropy alloys

Yin, J., Pei, Z., & Gao, M.C., (2021). Neural network-based order parameter for phase transitions and its applications in high-entropy alloys. Nature Computational Science, 1, 686-693. https//doi.org/10.1038/s43588-021-00139-3

Predicting temperature-dependent ultimate strengths of body-centered-cubic (BCC) high-entropy alloys

Steingrimsson, B., Fan, X., Yang, X., Gao, M.C., Zhang, Y., & Liaw, P.K., (2021). Predicting temperature-dependent ultimate strengths of body-centered-cubic (BCC) high-entropy alloys. npj Computational Materials, 7, 152. https://doi.org/10.1038/s41524-021-00623-4

Machine learning-informed ensemble framework for evaluating shale gas production potential: Case study in the Marcellus Shale

Vikara, D., Remson, D., & Khanna, V., (2020). Machine learning-informed ensemble framework for evaluating shale gas production potential: Case study in the Marcellus Shale. Journal of Natural Gas Science and Engineering, 84(12). https://doi.org/10.1016/j.jngse.2020.103679

Predicting Geologic Behavior in Carbon Storage Projects Using Graph Neural Network

Shih, C. Y., Holcomb, P., Liu, G., Siriwardane, H., Sethi, H., Nabian, M. (2024, March 20). Predicting Geologic Behavior in Carbon Storage Projects Using Graph Neural Network [Conference presentation]. 2024 GTC AI Conference. San Jose, CA.

Modeling the Cost of Onshore CO2 Pipeline Transport and Onshore CO2 Saline Storage

Morgan, D., Sheriff, A., Mark-Moser, M. K., Liu, G., Grant, T., Creason, C., Vikara, D., Cunha, L. (2024, March 13). Modeling the Cost of Onshore CO2 Pipeline Transport and Onshore CO2 Saline Storage [Conference presentation]. CCUS 2024. Houston, TX. https://www.osti.gov/biblio/2328141

An Insight-Centric Paradigm for Data Reduction and Inference Speed Improvement at the Scurry Area Canyon Reef Operator’s Committee (SACROC) Unit

Shih, C. Y., Wu, X., Liu, G., Siriwardane, H. (2024, March 11). An Insight-Centric Paradigm for Data Reduction and Inference Speed Improvement at the Scurry Area Canyon Reef Operator’s Committee (SACROC) Unit [Conference presentation]. CCUS 2024. Houston, TX. https://www.osti.gov/biblio/2324889

Physics-informed creep rupture life modeling of high temperature alloys for energy applications

Wenzlick, M., Trehern, W., Soares Chinen, A., Gao, M., Saidi, W. (2024, March 4). Physics-informed creep rupture life modeling of high temperature alloys for energy applications [Conference presentation]. Minerals, Metals, and Materials Society (TMS) Conference 2024. Orlando, FL.

Complementing the CCS Class VI Well Permit Process with DOE-NETL’s SMART Initiative Tools & Workflows

Siriwardane, H., Viswanathan, H., Hosseini, S. (2024, February 27). Complementing the CCS Class VI Well Permit Process with DOE-NETL’s SMART Initiative Tools & Workflows [Conference presentation]. Ground Water Protection Council (GWPC) 2024 Underground Injection Control (UIC) Conference. Oklahoma City, OK.

Quantifying Fracture Networks in CO2 Injection Zones: An Unsupervised Machine Learning Approach

Harbert, W., Myshakin, E., Liu, G., Siriwardane, H. (2024, January 11). Quantifying Fracture Networks in CO2 Injection Zones: An Unsupervised Machine Learning Approach [Conference presentation]. Machine Learning in Solid Earth Geoscience Conference. Santa Fe, NM.

A Machine Learning Approach For Well Integrity Prediction Using Cement Bond Logs​

Grabowski, O., Houghton, B., Pfander, I., Dilmore, R., Lackey, G. (2023, October 17). A Machine Learning Approach for Well Integrity Prediction Using Cement Bond Logs​ [Conference presentation]. Geological Society of America Annual Meeting. Pittsburgh, PA. https://www.netl.doe.gov/energy-analysis/details?id=a8f6f8e3-954d-415c-b8af-06eda014050c

Carbon Storage Technical Viability Approach (CS TVA): Multi-Factor Data Assessment Workflow to Determine Geologic Sequestration Feasibility

Mulhern, J., Mark-Moser, M. K., Creason, C., Shay, J., Rose, K. (2023, October 15). Carbon Storage Technical Viability Approach (CS TVA): Multi-Factor Data Assessment Workflow to Determine Geologic Sequestration Feasibility [Conference presentation]. Geological Society of America Annual Meeting. Pittsburgh, PA.

Process Cycle Modeling with AI

Romanov, V. (2023, October 1). Process Cycle Modeling with AI [Conference Presentation]. Materials Science & Technology (MS&T) 2023 Annual Meeting. Columbus, OH. https://www.osti.gov/servlets/purl/2337518.

An Introduction to NETL’s Science-based AI/ML Institute

An Introduction to NETL’s Science-based AI/ML Institute [Presentation], (2021, May 13).  https://netl.doe.gov/sites/default/files/netl-file/21AIML_Rose_0.pdf

News

Outreach Presentations
& Videos

Events &
Proceedings

Branding Guidelines