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
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
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
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
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
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
Alumbaugh, D., Gasperikova, E., Crandall, D., Commer, M., Feng, S., Harbert, W., Li, Y., Lin, Y., and Samarasinghe, S., (2023). The Kimberlina synthetic multiphysics dataset for CO2 monitoring investigations. Geoscience Data Journal. https://doi.org/10.1002/gdj3.191
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
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
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