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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

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

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

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

Liu, G., Kumar, A., Harbert, W., Siriwardane, H., Myshakin, E., Crandall, D., Cunha, L. (2024, June 23). Machine Learning Application for CCUS Carbon Storage: Fracture Analysis and Mapping in The Illinois Basin [Conference presentation]. 58th US Rock Mechanics/Geomechanics Symposium (ARMA). Golden, CO.

Machine-Learned Force Field Modeling of Metal Organic Frameworks for CO2 Direct Air Capture

Findley, J., Budhathoki, S., Steckel, J. (2024, June 19). Machine-Learned Force Field Modeling of Metal Organic Frameworks for CO2 Direct Air Capture [Conference presentation]. Clearwater Clean Energy Conference. Clearwater, FL. https://www.osti.gov/biblio/2375046

Modeling and Optimization of Zeolites for Contaminant Removal from Coal Combustion Impoundment Leachates

Findley, J., Grol, E., Granite, E., Steckel, J. (2024, June 18). Modeling and Optimization of Zeolites for Contaminant Removal from Coal Combustion Impoundment Leachates [Conference presentation]. Clearwater Clean Energy Conference. Clearwater, FL. https://www.osti.gov/biblio/2375006

A Methodology for Simulating Supercritical CO2 Heat Transfer Experiments Using Machine Learning Models

Grabowski, O., Searle, M., Straub, D. (2024, June 17). A Methodology for Simulating Supercritical CO2 Heat Transfer Experiments Using Machine Learning Models [Conference presentation]. Clearwater Clean Energy Conference. Clearwater, FL.

The Advanced Scale Up Reactor Experiment (ASURE) Facility: A Testbed for Advancing the Art of Biomass and Waste Co-Gasification Systems

Rowan, S., Breault, R. (2024, June 16). The Advanced Scale Up Reactor Experiment (ASURE) Facility: A Testbed for Advancing the Art of Biomass and Waste Co-Gasification Systems [Conference presentation]. Clearwater Clean Energy Conference. Clearwater, FL. https://www.osti.gov/biblio/2377348

Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits

Liu, G., Wu, X., Romanov, V. (2024, June 4). Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits [Conference presentation]. 5th Annual Appalachian Basin Geophysical Symposium. Canonsburg, PA. https://www.osti.gov/biblio/2370395

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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