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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

TEA of the CO2 capture process in pre-combustion applications using thirty-five physical solvents: Predictions with ANN

Husain E. Ashkanani, Rui Wang, Wei Shi, Nicholas S. Siefert, Robert L. Thompson, Kathryn H. Smith, Janice A. Steckel, Isaac K. Gamwo, David Hopkinson, Kevin Resnik, Badie I. Morsi, 2023, TEA of the CO2 capture process in pre-combustion applications using thirty-five physical solvents: Predictions with ANN, International Journal of Greenhouse Gas Control, Volume 130, 104007, ISSN 1750-5836. https://doi.org/10.1016/j.ijggc.2023.104007.

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

Liu, G., Kumar, A., Harbert, W., Siriwardane, H., Crandall, D., Bromhal, G., and L. Cunha. Machine Learning Application for CCUS Carbon Storage: Fracture Analysis and Mapping in the Illinois Basin [Conference Paper]. SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, October 2023. https://doi.org/10.2118/214996-MS

Optimization of Process Families for Deployment of Carbon Capture Processes Using Machine Learning Surrogates

Stinchfield, G., Ammari, B., Morgan, J.C., Siirola, J.D., Zamarripa, M., and C.D. Laird, (2023). Optimization of Process Families for Deployment of Carbon Capture Processes Using Machine Learning Surrogates. Proceedings of the 33rd European Symposium on Computer Aided Process Engineering (ESCAPE33), June 18-21, 2023, Athens, Greece. https://doi.org/10.1016/B978-0-443-15274-0.50212-2

Highly transferable atomistic machine-learning potentials from curated and compact datasets across the periodic table

Andolina, C.M., and Saidi, W.A., (2023). Highly transferable atomistic machine-learning potentials from curated and compact datasets across the periodic table. Digital Discovery, 2, 1070-1077. https://doi.org/10.1039/D3DD00046J

Metal hydride composition-derived parameters as machine learning features for material design and H2 storage

Nations, S., Nandi, T., Ramazani, A., Wang, S., and Duan, Y., (2023). Metal hydride composition-derived parameters as machine learning features for material design and H2 storage. Journal of Energy Storage, 107980. https://doi.org/10.1016/j.est.2023.107980

Machine learning data analytics based on distributed fiber sensors for pipeline feature detection

Zhang, P.D., Venketeswaran, A., Bukka, S.R., Sarcinelli, E., Lalam, N., Wright, R.F., and Ohodnicki, P.R., (2023). Machine learning data analytics based on distributed fiber sensors for pipeline feature detection. Proc. SPIE 12532, Optical Waveguide and Laser Sensors II. https://doi.org/10.1117/12.2663225

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

Kinetic Model Development and Bayesian Uncertainty Quantification for the Complete Reduction of Fe-based Oxygen Carriers with CH4, CO, and H2 for Chemical Looping Combustion

Ostace, A., Chen, Y.Y., Parker, R., Mebane, D.S., Okoli, C., Lee, A., Tong, A., Fan, L.S., Biegler, L.T., Burgard, A.P., Miller, D.C., & Bhattacharyya, D. (2021). Kinetic Model Development and Bayesian Uncertainty Quantification for the Complete Reduction of Fe-based Oxygen Carriers with CH4, CO, and H2 for Chemical Looping Combustion. Chemical Engineering Science, 252 (28), https://doi.org/10.1016/j.ces.2022.117512

Sensitivity Analysis of MFiX-PIC Parameters Using Nodeworks, PSUADE, and DAKOTA

Gel, A., Weber, J., & Vaidheeswaran, A., (2021). Sensitivity Analysis of MFiX-PIC Parameters Using Nodeworks, PSUADE, and DAKOTA, National Energy Technology Laboratory, DOE/NETL-2021.2652, Pittsburgh, PA. https://doi.org/10.2172/1809024

Evaluating proxies for the drivers of natural gas productivity using machine-learning models

Kumar, A., Harbert, W., Hammack, R., Zorn, E., Bear, A., & Carr, T. (2021). Evaluating proxies for the drivers of natural gas productivity using machine-learning models. Interpretation, 9(4). https://doi.org/10.1190/INT-2020-0200.1

Evaluating Offshore Infrastructure Integrity

Nelson, J., Dyer, A., Romeo, L., Wenzlick, M., Zaengle, D., Duran, R,. Sabbatino, M., Wingo, P., Barkhurst, A., Rosse, K., & Bauer, J., (2021). Evaluating Offshore Infrastructure Integrity, National Energy Technology Laboratory, DOE/NETL-2021/2643, Albany, OR. https://doi.org/10.2172/1780656

Machine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels

Mamun, O., Wenzlick, M., Sathanur, A., Hawk, J., & Devanathan, R., (2021). Machine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels. npj Materials Degradation, 5, 20. https://doi.org/10.1038/s41529-021-00166-5

Data science techniques, assumptions, and challenges in alloy clustering and property prediction

Wenzlick, M., Mamun, O., Devanathan, R., Rose, K., & Hawk, J., (2021). Data science techniques, assumptions, and challenges in alloy clustering and property prediction. Journal of Materials Engineering and Performance 30, 823–838. https://doi.org/10.1007/s11665-020-05340-5

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

CARD: CFD for Advanced Reactor Design

Dietiker, J. (2024, April 25). CARD: CFD for Advanced Reactor Design [Conference presentation]. 2024 FECM Spring R&D Project Review Meeting. Pittsburgh, PA. https://www.osti.gov/biblio/2339843

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

Bukka, S. R., Lalam, N., Bhatta, H., Wright, R. (2024, April 24). Lab Scale Demonstration of Pipeline Third-Party Damage Classification Using Convolutional Neural Networks [Conference presentation]. SPIE Defense + Commercial Sensing. National Harbor, MD. https://www.osti.gov/biblio/2340060

Deploying a New AI Software Tool for Rapid Characterization & Quantification of Unconventional Sources of Critical Minerals

Creason, C., Rose, K., Montross, S., Maymi, N., Jackson, Z., Obarr, S., Bishop, E., Wingo, P., Hazle, G., Skipwith, S., Moyes, A., Lindemann, G., Atkins, C., Hird, J., Taglia, F. (2024, April 4). Deploying a New AI Software Tool for Rapid Characterization & Quantification of Unconventional Sources of Critical Minerals [Conference presentation]. 2024 NETL Resource Sustainability Project Review Meeting. Pittsburgh, PA. https://www.osti.gov/biblio/2338061

Produced Water Research Partnership

Siefert, N. (2024, April 3). Produced Water Research Partnership [Conference presentation]. 2024 NETL Resource Sustainability Project Review Meeting. Pittsburgh, PA.

Project PARETO – DOE’s Produced Water Optimization Initiative

Shamlou, E., Zamarripa, M., Arnold, T., Tominac, P., Shellman, M., Drouven, M. (2024, April 3). Project PARETO – DOE’s Produced Water Optimization Initiative [Conference presentation]. 2024 NETL Resource Sustainability Project Review Meeting. Pittsburgh, PA. https://www.osti.gov/biblio/2341292

Critical Minerals: Systems Analysis Tasks

Fritz, A., Pickenpaugh, G., Creason, C., Suter, J., Krynock, M., Able, C. (2024, April 2). Critical Minerals: Systems Analysis Tasks [Conference presentation]. 2024 NETL Resource Sustainability Project Review Meeting. Pittsburgh, PA. https://www.osti.gov/biblio/2337611

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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