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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

UNet Performance with Wafer Scale Engine (Optimization Case Study)

Romanov, V. (2023). UNet Performance with Wafer Scale Engine (Optimization Case Study). 2023 IEEE High Performance Extreme Computing Conference (HPEC), 1–6. https://doi.org/10.1109/HPEC58863.2023.10363451

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

Hoover, B., Zaengle, D., Mark-Moser, M., Wingo, P., Suhag, A., and Rose, K., (2023). Enhancing knowledge discovery from unstructured data using a deep learning approach to support subsurface modeling predictions. Frontiers. Big Data 6:1227189. https://doi.org/10.3389/fdata.2023.1227189

Assessing Pore Network Heterogeneity Across Multiple Scales to Inform CO2 Injection Models

Butler, S.K., Barajas-Olalde, C., Yu, X., Mibeck, B.A.F., Burton-Kelly, M.E., Kong, L., Kurz, B., Crandall, D. (2023) Assessing Pore Network Heterogeneity Across Multiple Scales to Inform CO2 Injection Models, International Journal of Greenhouse Gas Control, 130, 104017 https://doi.org/10.1016/j.ijggc.2023.104017

Exploring the formation of gold/silver nanoalloys with gas-phase synthesis and machine-learning assisted simulations

Gromoff, Q., Benzo, P., Saidi, W.A., Andolina, C.M., Casanove, M.J., Hungria, T., Barre, S., Benoit, M., and Lam, J., (2023). Exploring the formation of gold/silver nanoalloys with gas-phase synthesis and machine-learning assisted simulations. Nanoscale, 16(1), 384-393. https://doi.org/10.1039/D3NR04471H

Enhanced CO2 Reactive Capture and Conversion Using Aminothiolate Ligand–Metal Interface

Wan, M., Yang, Z., Morgan, H., Shi, J., Shi, F., Liu, M., Wong, H.W., Gu, Z., and Che, F., (2023). Enhanced CO2 Reactive Capture and Conversion Using Aminothiolate Ligand–Metal Interface. Journal of the American Chemical Society, 145(48), 26038-26051. https://doi.org/10.1021/jacs.3c06888

Machine-Learning-Based Rotating Detonation Engine Diagnostics: Evaluation for Application in Experimental Facilities

Johnson, K. B., Ferguson, D., and Nix, A., (2023). Machine-Learning-Based Rotating Detonation Engine Diagnostics: Evaluation for Application in Experimental Facilities. Journal of Propulsion and Power, 1-14. https://doi.org/10.2514/1.B39287

Adversarial Ensemble Modeling of Multi-modal Mechanical Properties for Iron-Based Alloys

Romanov, V., (2022). Adversarial Ensemble Modeling of Multi-modal Mechanical Properties for Iron-Based Alloys. JOM, 74(4). https://doi.org/10.1007/s11837-022-05163-w

Reinforcement learning for online adaptation of model predictive controllers: Application to a selective catalytic reduction unit

Hedrick, E., Hedrick, K., Bhattacharyya, D., Zitney, S.E., & Omell, B., (2022). Reinforcement learning for online adaptation of model predictive controllers: Application to a selective catalytic reduction unit, Computers & Chemical Engineering, 160, ISSN 0098-1354, https://doi.org/10.1016/j.compchemeng.2022.107727

Applied machine learning model comparison: Predicting offshore platform integrity with gradient boosting algorithms and neural networks

Dyer, A.S., Zaengle, D., Nelson, J.R., Duran, R., Wenzlick, M., Wingo, P.C., Bauer, J.R., Rose, K., & Romeo, L. (2022). Applied machine learning model comparison: Predicting offshore platform integrity with gradient boosting algorithms and neural networks. Marine Structures, 83. https://doi.org/10.1016/j.marstruc.2021.103152

Simulation microstructure evolution in Ni-YSZ electrodes of solid oxide cells under operating conditions

Lei, Y., Epting, W., Mason, J., Cheng, T., Abernathy, H., Hackett, G., & Wen, Y., (2022). Simulation microstructure evolution in Ni-YSZ electrodes of solid oxide cells under operating conditions. TMS 2022 151st Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals, & Materials Series. Springer Cham, 457-469, https://doi.org/10.1007/978-3-030-92381-5_42

Simulating microstructure evolution in Ni-YSZ electrodes of solid oxide cells under operating conditions

Lei, Y., Epting, W., Mason, J., Cheng, T., Abernathy, H., Hackett, G., & Wen, Y., (2022). Simulating microstructure evolution in Ni-YSZ electrodes of solid oxide cells under operating conditions. TMS 2022 151st Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals, & Materials Series. Springer Cham, 457-469, https://doi.org/10.1007/978-3-030-92381-5_42

Benefit Analysis of CO2 Delivery Options for Offshore Storage or Enhanced Oil Recovery

Shih, C., Lin, S., Milligan, M., Wendt, A., Marquis, M., Eppink, J., & Grant, T., (2022), Benefit Analysis of CO2 Delivery Options for Offshore Storage or Enhanced Oil Recovery, National Energy Technology Laboratory, DOE/NETL-2022/3792, Pittsburgh, PA.

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

Cell and Stack Degradation Evaluation and Modeling

Abernathy, H. (2024, May 7). Cell and Stack Degradation Evaluation and Modeling [Conference presentation]. 2024 Hydrogen Annual Merit Review. Crystal City, VA. https://www.hydrogen.energy.gov/docs/hydrogenprogramlibraries/pdfs/review24/fe008_abernathy_2024_o.pdf?sfvrsn=85e66a06_3

AI-Driven Breakthroughs in Energy Systems from Vision to Design

Weber, J. (2024, May 7). AI-Driven Breakthroughs in Energy Systems from Vision to Design [Conference presentation]. AI Expo. Washington, DC.

Advanced Offshore Hazard Forecasting to Enable Resilient Offshore Operations

Mark-Moser, M. K., Romeo, L., Duran, R., Bauer, J., Rose, K. (2024, May 6). Advanced Offshore Hazard Forecasting to Enable Resilient Offshore Operations [Conference presentation]. Offshore Technology Conference 2024. Houston, TX. https://www.osti.gov/biblio/2352616

Rapid Assessment and Optimization of SOC Electrodes from Low Resolution Data Using Machine Learning and Computer Vision

Epting, W. (2024, May 1). Rapid Assessment and Optimization of SOC Electrodes from Low Resolution Data Using Machine Learning and Computer Vision [Conference presentation]. 2024 DICE Digital Engineering Conference. Idaho Falls, ID.

AI/ML challenges and opportunities in materials development

Wenzlick, M., Trehern, W., Saidi, W. (2024, April 30). AI/ML challenges and opportunities in materials development [Conference presentation]. 2024 DICE Digital Engineering Conference. Idaho Falls, ID.

Degradation modeling and electrode engineering of SOFCs, SOECs, and R-SOCs

Abernathy, H., Epting, W., Lei, Y., Liu, J. (2024, April 25). Degradation modeling and electrode engineering of SOFCs, SOECs, and R-SOCs [Conference presentation]. 2024 FECM Spring R&D Project Review Meeting. Pittsburgh, PA. https://www.osti.gov/biblio/2342141

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

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