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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

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

Application of machine learning to characterize gas hydrate reservoirs in Mackenzie Delta (Canada) and on the Alaska north slope (USA)

Leebyn, C., Harpreet, S., Creason, C.G., Seol, Y., and Myshakin, E.M., 2022, Application of machine learning to characterize gas hydrate reservoirs in Mackenzie Delta (Canada) and on the Alaska north slope (USA). Commputational Geosciences, 326, 1151-1165. https://doi.org/10.1007/s10596-022-10151-9

Deep-learning-based workflow for boundary and small target segmentation in digital rock images using UNet++ and IK-EBM

Wang, H., Dalton, L., Fan, M., Guo, R., McClure, J., Crandall, D., and Chen, C., (2022). Deep-learning-based workflow for boundary and small target segmentation in digital rock images using UNet++ and IK-EBM. Journal of Petroleum Science and Engineering. 215, A. https://doi.org/10.1016/j.petrol.2022.110596

Emergence of local scaling relations in adsorption energies on high-entropy alloy

Saidi, W., (2022). Emergence of local scaling relations in adsorption energies on high-entropy alloys. npj Computational Materials, 8, 86. https://doi.org/10.1038/s41524-022-00766-y

Adapting Technology Learning Curves for Prospective Techno-Economic and Life Cycle Assessments of Emerging Carbon Capture and Utilization Pathways

Faber, G., Ruttinger, A., Strunge, T., Langhorst, T., Zimmermann, A., van der Hulst, M., Bensebaa, F., Moni, S., & Tao, L. (2022). Adapting Technology Learning Curves for Prospective Techno-Economic and Life Cycle Assessments of Emerging Carbon Capture and Utilization Pathways. Frontiers in Climate, 4. https://doi.org/10.3389/fclim.2022.820261

Evaluating the Impact of Proprietary Oil & Gas Data on Machine Learning Model Performance Using a Quasiexperimental Analytical Approach

Vikara, D., Bello, K., Wijaya, N., Warner, T., Sheriff, A., & Remson, D., (2022). Evaluating the Impact of Proprietary Oil & Gas Data on Machine Learning Model Performance Using a Quasiexperimental Analytical Approach. National Energy Technology Laboratory, Pittsburgh, PA, March 31, 2022. DOI: 10.2172/1855950

Dimensionally Reduced Model for Rapid and Accurate Prediction of Gas Saturation, Pressure, and Brine Production in a CO2 Storage Application: Case Study Using the SACROC Field as Part of SMART Task 5

Bello, K., Vikara, D., Morgan, D., & Remson, D., (2022). Dimensionally Reduced Model for Rapid and Accurate Prediction of Gas Saturation, Pressure, and Brine Production in a CO2 Storage Application: Case Study Using the SACROC Field as Part of SMART Task 5, National Energy Technology Laboratory, Pittsburgh, March 2022. https://doi.org/10.2172/1855950

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

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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