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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

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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