Hajiali, Faezeh; Ellis, Naoko; Gopaluni, Bhushan (2025) From biomass waste to CO2 capture: a multi-fidelity machine learning workflow for high-throughput screening of activated carbons. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01786-0
| Reference Type | Journal (article/letter/editorial) | ||
|---|---|---|---|
| Title | From biomass waste to CO2 capture: a multi-fidelity machine learning workflow for high-throughput screening of activated carbons | ||
| Journal | npj Computational Materials | ||
| Authors | Hajiali, Faezeh | Author | |
| Ellis, Naoko | Author | ||
| Gopaluni, Bhushan | Author | ||
| Year | 2025 (November 24) | Volume | 11 |
| Issue | 1 | ||
| Publisher | Springer Science and Business Media LLC | ||
| DOI | doi:10.1038/s41524-025-01786-0Search in ResearchGate | ||
| Generate Citation Formats | |||
| Mindat Ref. ID | 19272041 | Long-form Identifier | mindat:1:5:19272041:4 |
| GUID | 0 | ||
| Full Reference | Hajiali, Faezeh; Ellis, Naoko; Gopaluni, Bhushan (2025) From biomass waste to CO2 capture: a multi-fidelity machine learning workflow for high-throughput screening of activated carbons. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01786-0 | ||
| Plain Text | Hajiali, Faezeh; Ellis, Naoko; Gopaluni, Bhushan (2025) From biomass waste to CO2 capture: a multi-fidelity machine learning workflow for high-throughput screening of activated carbons. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01786-0 | ||
| In | (2025, January) npj Computational Materials Vol. 11 (1). Springer Science and Business Media LLC | ||
References Listed
These are the references the publisher has listed as being connected to the article. Please check the article itself for the full list of references which may differ. Not all references are currently linkable within the Digital Library.
| Laboratory, N. G. M. Trends in atmospheric carbon dioxide. https://gml.noaa.gov/ccgg/trends/. | |
![]() | Grubler, Arnulf, Wilson, Charlie, Bento, Nuno, Boza-Kiss, Benigna, Krey, Volker, McCollum, David L., Rao, Narasimha D., Riahi, Keywan, Rogelj, Joeri, De Stercke, Simon, Cullen, Jonathan, Frank, Stefan, Fricko, Oliver, Guo, Fei, Gidden, Matt, Havlík, Petr, Huppmann, Daniel, Kiesewetter, Gregor, Rafaj, Peter, Schoepp, Wolfgang, Valin, Hugo (2018) A low energy demand scenario for meeting the 1.5 °C target and sustainable development goals without negative emission technologies. Nature Energy, 3 (6) 515-527 doi:10.1038/s41560-018-0172-6 |
![]() | |
| Not Yet Imported: Industrial & Engineering Chemistry Research - journal-article : 10.1021/ie200686q If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
![]() | |
| Not Yet Imported: - journal-article : 10.1039/D3GC00636K If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
| Not Yet Imported: - book : 10.1596/978-1-4648-1329-0 If you would like this item imported into the Digital Library, please contact us quoting Book ID 9781464813290 | |
| Not Yet Imported: - journal-article : 10.1016/j.rser.2022.112413 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
![]() | |
| Not Yet Imported: - journal-article : 10.1016/j.seppur.2020.118065 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
![]() | |
![]() | Raccuglia, Paul, Elbert, Katherine C., Adler, Philip D. F., Falk, Casey, Wenny, Malia B., Mollo, Aurelio, Zeller, Matthias, Friedler, Sorelle A., Schrier, Joshua, Norquist, Alexander J. (2016) Machine-learning-assisted materials discovery using failed experiments. Nature, 533 (7601). 73-76 doi:10.1038/nature17439 |
| Not Yet Imported: Journal of Big Data - journal-article : 10.1186/s40537-021-00444-8 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
| Not Yet Imported: - journal-article : 10.1016/j.ymssp.2020.107398 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
| Not Yet Imported: - journal-article : 10.1109/TNNLS.2021.3084827 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
| Not Yet Imported: - monograph : 10.1017/9781108348973 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
| Frazier, P. I. A tutorial on Bayesian optimization. arXiv preprint arXiv:1807.02811 (2018). | |
| Snoek, J., Larochelle, H. & Adams, R. P. Practical Bayesian optimization of machine learning algorithms. Advances in neural information processing systems 25 (2012). | |
![]() | Langner, Stefan, Häse, Florian, Perea, José Darío, Stubhan, Tobias, Hauch, Jens, Roch, Loïc M., Heumueller, Thomas, Aspuru‐Guzik, Alán, Brabec, Christoph J. (2020) Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems. Advanced Materials, 32 (14) 1907801pp. doi:10.1002/adma.201907801 |
![]() | |
![]() | Pedersen, Jack K., Clausen, Christian M., Krysiak, Olga A., Xiao, Bin, Batchelor, Thomas A. A., Löffler, Tobias, Mints, Vladislav A., Banko, Lars, Arenz, Matthias, Savan, Alan, Schuhmann, Wolfgang, Ludwig, Alfred, Rossmeisl, Jan (2021) Bayesian Optimization of High‐Entropy Alloy Compositions for Electrocatalytic Oxygen Reduction**. Angewandte Chemie, 133 (45) 24346-24354 doi:10.1002/ange.202108116 |
| Ramos, M. C., Michtavy, S. S., Porosoff, M. D. & White, A. D. Bayesian optimization of catalysts with in-context learning. arXiv preprint arXiv:2304.05341 (2023). | |
![]() | Kusne, A. Gilad, Yu, Heshan, Wu, Changming, Zhang, Huairuo, Hattrick-Simpers, Jason, DeCost, Brian, Sarker, Suchismita, Oses, Corey, Toher, Cormac, Curtarolo, Stefano, Davydov, Albert V., Agarwal, Ritesh, Bendersky, Leonid A., Li, Mo, Mehta, Apurva, Takeuchi, Ichiro (2020) On-the-fly closed-loop materials discovery via Bayesian active learning. Nature Communications, 11 (1) doi:10.1038/s41467-020-19597-w |
| Not Yet Imported: - journal-article : 10.1016/j.trechm.2020.11.004 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
![]() | Liang, Qiaohao, Gongora, Aldair E., Ren, Zekun, Tiihonen, Armi, Liu, Zhe, Sun, Shijing, Deneault, James R., Bash, Daniil, Mekki-Berrada, Flore, Khan, Saif A., et al. (2021) Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains. npj Computational Materials, 7. doi:10.1038/s41524-021-00656-9 |
![]() | |
| Not Yet Imported: - journal-article : 10.1016/j.matt.2021.06.036 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
| Kandasamy, K., Dasarathy, G., Schneider, J. & Póczos, B. Multi-fidelity Bayesian optimisation with continuous approximations. In International conference on machine learning, 1799–1808 (PMLR, 2017). | |
| Not Yet Imported: - journal-article : 10.1115/1.4046697 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
| Not Yet Imported: Digital Discovery - journal-article : 10.1039/D3DD00117B If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
| Judge, E., Azzouzi, M., Mroz, A. M., Chanona, A. D. R. & Jelfs, K. E. Applying multi-fidelity Bayesian optimization in chemistry: Open challenges and major considerations. arXiv preprint arXiv:2409.07190 (2024). | |
| Wu, J., Toscano-Palmerin, S., Frazier, P. I. & Wilson, A. G. Practical multi-fidelity Bayesian optimization for hyperparameter tuning. In Adams, R. P. & Gogate, V. (eds.) Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, vol. 115 of Proceedings of Machine Learning Research, 788–798 (PMLR, 2020). | |
| Not Yet Imported: - journal-article : 10.1007/s00158-005-0587-0 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
![]() | |
![]() | |
![]() | |
![]() | |
| Not Yet Imported: Separation and Purification Technology - journal-article : 10.1016/j.seppur.2022.122521 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
| Not Yet Imported: - journal-article : 10.1038/s43588-020-00002-x If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
![]() | |
![]() | Otchere, Daniel Asante, Ganat, Tarek Omar Arbi, Ojero, Jude Oghenerurie, Tackie-Otoo, Bennet Nii, Taki, Mohamed Yassir (2022) Application of gradient boosting regression model for the evaluation of feature selection techniques in improving reservoir characterisation predictions. Journal of Petroleum Science and Engineering, 208. 109244pp. doi:10.1016/j.petrol.2021.109244 |
![]() | |
| Hastie, T. The elements of statistical learning: data mining, inference, and prediction (2009). | |
| Bergstra, J. & Bengio, Y. Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281–305 (2012). | |
| Not Yet Imported: Bioresource Technology - journal-article : 10.1016/j.biortech.2024.130624 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
| Li, H., Ng, J. Y.-H. & Natsev, P. Ensemblenet: End-to-end optimization of multi-headed models. arXiv preprint arXiv:1905.09979 (2019). | |
| Jin, H., Vuong, Q. & Abbeel, P. Keras tuner. https://keras.io/keras_tuner/ (2020). | |
| Goodfellow, I. Deep learning (2016). | |
| Wu, J., Toscano-Palmerin, S., Frazier, P. I. & Wilson, A. G. Practical multi-fidelity Bayesian optimization for hyperparameter tuning. In Uncertainty in Artificial Intelligence, 788–798 (PMLR, 2020). | |
| Mikkola, P., Martinelli, J., Filstroff, L. & Kaski, S. Multi-fidelity Bayesian optimization with unreliable information sources. In International Conference on Artificial Intelligence and Statistics, 7425–7454 (PMLR, 2023). | |
| Not Yet Imported: - journal-article : 10.1002/adfm.202204714 If you would like this item imported into the Digital Library, please contact us quoting Journal ID |
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