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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

UNet Performance with Wafer Scale Engine (Optimization Case Study)

Romanov, V. (2023). UNet Performance with Wafer Scale Engine (Optimization Case Study). 2023 IEEE High Performance Extreme Computing Conference (HPEC), 1–6. https://doi.org/10.1109/HPEC58863.2023.10363451

Enhancing knowledge discovery from unstructured data using a deep learning approach to support subsurface modeling predictions

Hoover, B., Zaengle, D., Mark-Moser, M., Wingo, P., Suhag, A., and Rose, K., (2023). Enhancing knowledge discovery from unstructured data using a deep learning approach to support subsurface modeling predictions. Frontiers. Big Data 6:1227189. https://doi.org/10.3389/fdata.2023.1227189

Assessing Pore Network Heterogeneity Across Multiple Scales to Inform CO2 Injection Models

Butler, S.K., Barajas-Olalde, C., Yu, X., Mibeck, B.A.F., Burton-Kelly, M.E., Kong, L., Kurz, B., Crandall, D. (2023) Assessing Pore Network Heterogeneity Across Multiple Scales to Inform CO2 Injection Models, International Journal of Greenhouse Gas Control, 130, 104017 https://doi.org/10.1016/j.ijggc.2023.104017

Exploring the formation of gold/silver nanoalloys with gas-phase synthesis and machine-learning assisted simulations

Gromoff, Q., Benzo, P., Saidi, W.A., Andolina, C.M., Casanove, M.J., Hungria, T., Barre, S., Benoit, M., and Lam, J., (2023). Exploring the formation of gold/silver nanoalloys with gas-phase synthesis and machine-learning assisted simulations. Nanoscale, 16(1), 384-393. https://doi.org/10.1039/D3NR04471H

Enhanced CO2 Reactive Capture and Conversion Using Aminothiolate Ligand–Metal Interface

Wan, M., Yang, Z., Morgan, H., Shi, J., Shi, F., Liu, M., Wong, H.W., Gu, Z., and Che, F., (2023). Enhanced CO2 Reactive Capture and Conversion Using Aminothiolate Ligand–Metal Interface. Journal of the American Chemical Society, 145(48), 26038-26051. https://doi.org/10.1021/jacs.3c06888

Machine-Learning-Based Rotating Detonation Engine Diagnostics: Evaluation for Application in Experimental Facilities

Johnson, K. B., Ferguson, D., and Nix, A., (2023). Machine-Learning-Based Rotating Detonation Engine Diagnostics: Evaluation for Application in Experimental Facilities. Journal of Propulsion and Power, 1-14. https://doi.org/10.2514/1.B39287

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

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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