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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

Application of machine learning to characterize gas hydrate reservoirs in Mackenzie Delta (Canada) and on the Alaska north slope (USA)

Leebyn, C., Harpreet, S., Creason, C.G., Seol, Y., and Myshakin, E.M., 2022, Application of machine learning to characterize gas hydrate reservoirs in Mackenzie Delta (Canada) and on the Alaska north slope (USA). Commputational Geosciences, 326, 1151-1165. https://doi.org/10.1007/s10596-022-10151-9

Deep-learning-based workflow for boundary and small target segmentation in digital rock images using UNet++ and IK-EBM

Wang, H., Dalton, L., Fan, M., Guo, R., McClure, J., Crandall, D., and Chen, C., (2022). Deep-learning-based workflow for boundary and small target segmentation in digital rock images using UNet++ and IK-EBM. Journal of Petroleum Science and Engineering. 215, A. https://doi.org/10.1016/j.petrol.2022.110596

Emergence of local scaling relations in adsorption energies on high-entropy alloy

Saidi, W., (2022). Emergence of local scaling relations in adsorption energies on high-entropy alloys. npj Computational Materials, 8, 86. https://doi.org/10.1038/s41524-022-00766-y

Adapting Technology Learning Curves for Prospective Techno-Economic and Life Cycle Assessments of Emerging Carbon Capture and Utilization Pathways

Faber, G., Ruttinger, A., Strunge, T., Langhorst, T., Zimmermann, A., van der Hulst, M., Bensebaa, F., Moni, S., & Tao, L. (2022). Adapting Technology Learning Curves for Prospective Techno-Economic and Life Cycle Assessments of Emerging Carbon Capture and Utilization Pathways. Frontiers in Climate, 4. https://doi.org/10.3389/fclim.2022.820261

Evaluating the Impact of Proprietary Oil & Gas Data on Machine Learning Model Performance Using a Quasiexperimental Analytical Approach

Vikara, D., Bello, K., Wijaya, N., Warner, T., Sheriff, A., & Remson, D., (2022). Evaluating the Impact of Proprietary Oil & Gas Data on Machine Learning Model Performance Using a Quasiexperimental Analytical Approach. National Energy Technology Laboratory, Pittsburgh, PA, March 31, 2022. DOI: 10.2172/1855950

Dimensionally Reduced Model for Rapid and Accurate Prediction of Gas Saturation, Pressure, and Brine Production in a CO2 Storage Application: Case Study Using the SACROC Field as Part of SMART Task 5

Bello, K., Vikara, D., Morgan, D., & Remson, D., (2022). Dimensionally Reduced Model for Rapid and Accurate Prediction of Gas Saturation, Pressure, and Brine Production in a CO2 Storage Application: Case Study Using the SACROC Field as Part of SMART Task 5, National Energy Technology Laboratory, Pittsburgh, March 2022. https://doi.org/10.2172/1855950

Gaining Perspective on Unconventional Well Design Choices through Play-level Application of Machine Learning Modeling

Vikara, D., Remson, D., & Khanna, V., (2021). Gaining Perspective on Unconventional Well Design Choices through Play-level Application of Machine Learning Modeling. Upstream Oil and Gas Technology, 4. https://doi.org/10.1016/j.upstre.2020.100007

Leak detection in a subcritical boiler

Panday, R., Shadle, L. J., Indrawan, N., and Vesel, R. W., (2021). Leak detection in a subcritical boiler. Applied Thermal Engineering, 185(116371). https://doi.org/10.1016/j.applthermaleng.2020.116371

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

An Environmental, Energy, Economic, and Social Justice Database for Carbon Capture and Storage Applications

Sharma, M., White, C., Cleaveland, C., Romeo, L., Rose, K., Bauer, J. (2023, December 11). An Environmental, Energy, Economic, and Social Justice Database for Carbon Capture and Storage Applications [Conference presentation]. American Geophysical Union (AGU) Fall Meeting 2023. San Francisco, CA.

Machine Learning for Oil and Gas Well Identification in Historic Maps

Mundia-Howe, M., Houghton, B., Shay, J., Bauer, J. (2023, November 8). Machine Learning for Oil and Gas Well Identification in Historic Maps [Conference presentation]. University of Pittsburgh Infrastructure Sensor Collaboration 2023 Workshop. Pittsburgh, PA. https://www.netl.doe.gov/energy-analysis/details?id=5236c646-64e1-4846-be19-05138673c970

Integrating Public and Private Data for Modeling and Optimization of Shale Oil and Gas Production

Romanov, V., Vikara, D. M., Bello, K., Mohaghegh, S. D., Liu, G., Cunha, L. (2024, November 7). Integrating Public and Private Data for Modeling and Optimization of Shale Oil and Gas Production [Conference presentation]. 2023 AIChE Annual Meeting. Orlando, FL. https://www.osti.gov/biblio/2336703

Heat Transfer Opportunities for Supercritical CO2 Power Systems

Searle, M., Grabowski, O., Tulgestke, A., Weber, J., Straub, D. (2023, October 30). Heat Transfer Opportunities for Supercritical CO2 Power Systems [Conference presentation]. 2023 University Turbine Systems Research (UTSR) and Advanced Turbines Program Review. State College, PA. https://www.netl.doe.gov/energy-analysis/details?id=ec1106ec-bddb-4030-a176-ad20ca9f5ffd

Machine Learning Application for CCUS Carbon Storage: Fracture Analysis and Mapping in The Illinois Basin

Liu, G., Kumar, A., Harbert, W., Myshakin, E., Siriwardane, H., Bromhal, G., Cunha, L. (2023, October 18). Machine Learning Application for CCUS Carbon Storage: Fracture Analysis and Mapping in The Illinois Basin [Conference presentation]. 2023 SPE Annual Technical Conference and Exhibition (ATCE). San Antonio, TX.

A Multi-scale, Geo-data Science Method for Assessing Unconventional Critical Mineral Resources

Creason, C. G., Justman, D., Yesenchak, R., Montross, S., Wingo, P., Thomas, R. B., Rose, K. (2023, October 17). A Multi-scale, Geo-data Science Method for Assessing Unconventional Critical Mineral Resources [Conference presentation]. Geological Society of America Annual Meeting. Pittsburgh, PA.

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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