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Theoretical Prediction of Thermal Expansion Anisotropy for Y2Si2O7 Environmental Barrier Coatings Using a Deep Neural Network Potential and Comparison to Experiment

Bodenschatz, Cameron J., Wissam A. Saidi, Jamesa L. Stokes, Rebekah I. Webster, and Gustavo Costa. 2024. “Theoretical Prediction of Thermal Expansion Anisotropy for Y2Si2O7 Environmental Barrier Coatings Using a Deep Neural Network Potential and Comparison to Experiment” Materials 17, no. 2: 286. https://doi.org/10.3390/ma17020286

Machine Learning and Deep Learning for Mineralogy Interpretation and CO2 Saturation Estimation in Geological Carbon Storage: A Case Study in the Illinois Basin

Wang, H., Williams-Stroud, S., Crandall, D., and Chen, C. (2024). Machine learning and deep learning for mineralogy interpretation and CO2 saturation estimation in geological carbon Storage: A case study in the Illinois Basin. Fuel, 361(130586). https://doi.org/10.1016/j.fuel.2023.130586

Beyond price taker: Conceptual design and optimization of integrated energy systems using machine learning market surrogates

Jalving, J., Ghouse J., Cortes, N., Gao, X., Knueven, B., Agi, D., Martin, S., Chen, X.H., Guittet, D., Tumbalam-Gooty, R., Bianchi, L., Beattie, K., Gunter, D., Siirola, J.D., Miller, D.C., and Dowling, A.W., (2023). Beyond price taker: Conceptual design and optimization of integrated energy systems using machine learning market surrogates. Applied Energy, 351. DOI10.1016/j.apenergy.2023.121767

Machine-Learning Accelerated First-Principles Accurate Modeling of the Solid–Liquid Phase Transition in MgO under Mantle Conditions

Wisesa, P., Andolina, C.M., and Saidi, W.A., (2023). Machine-Learning Accelerated First-Principles Accurate Modeling of the Solid–Liquid Phase Transition in MgO under Mantle Conditions, The Journal of Physical Chemistry Letters, 14 (39), 8741-8748. DOI: 10.1021/acs.jpclett.3c02424

Robust Vector BOTDA Signal Processing with Probabilistic Machine Learning

Venketeswaran, A., Lalam, N., Lu., P., Bukka, S.R., Buric, M.P., and Wright, R., (2023). Robust Vector BOTDA Signal Processing with Probabilistic Machine Learning. Sensors, 23 (13). DOI:10.3390/s23136064

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

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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