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A machine learning approach for determining temperature-dependent bandgap of metal oxides utilizing Allen–Heine–Cardona theory and O’Donnell model parameterization

Nandi, T., Chong, L., Park, J., Saidi, W.A., Chorpening, B., Bayham, S., and Duan, Y. (2024) A machine learning approach for determining temperature-dependent bandgap of metal oxides utilizing Allen–Heine–Cardona theory and O’Donnell model parameterization. AIP Advances, 14, 035231. https://doi.org/10.1063/5.0190024

Offshore application of landslide susceptibility mapping using gradient-boosted decision trees: a Gulf of Mexico case study

Dyer, A.S., Mark-Moser, M., Duran, R., and Bauer, J.R., 2024, Offshore application of landslide susceptibility mapping using gradient-boosted decision trees: a Gulf of Mexico case study. Natural Hazards. https://doi.org/10.1007/s11069-024-06492-6

Machine Learning Design of Perovskite Catalytic Properties

Jacobs, R., Liu, J., Abernathy, H., and Morgan, D. (2024). Machine Learning Design of Perovskite Catalytic Properties. Advanced Energy Materials. https://doi.org/10.1002/aenm.202303684

Machine Learning Application to Assess Occurrence and Saturations of Methane Hydrate in Marine Deposits Offshore India

Chong, L., Collett, T.S., Creason, C.G., Seol, Y., and Myshakin, E.M., (2024). Machine Learning Application to Assess Occurrence and Saturations of Methane Hydrate in Marine Deposits Offshore India. Interpretation, 0. https://doi.org/10.1190/int-2023-0056.1

Creation of Polymer Datasets with Targeted Backbones for Screening of High-Performance Membranes for Gas Separation

Tiwari, S.P., Shi, W., Budhathoki, S., Baker, J., Sekizkardes, A.K., Zhu, L., Kusuma, V.A., Hopkinson, D.P., and Steckel, J.A., 2024, Creation of Polymer Datasets with Targeted Backbones for Screening of High-Performance Membranes for Gas Separation. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs.jcim.3c01232

High-throughput ab initio calculations and machine learning to discover SrFeO3-δ-based perovskites for chemical-looping applications

Ramanzi, A., Duell, B.A., Popczun, E.J., Natesakhawat, S., Nandi, T., Lekse, J.W., and Duan, Y. (2024). High-throughput ab initio calculations and machine learning to discover SrFeO3-δ-based perovskites for chemical-looping applications. Cell Reports Physical Science, 5(2), 101797. https://doi.org/10.1016/j.xcrp.2024.101797

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

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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