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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

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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