5  Next Steps

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5.1 HydraGNN

Blanchard, A. E., Shekar, M. C., Gao, S., Gounley, J., Lyngaas, I., Glaser, J., & Bhowmik, D. (2022). Automating Genetic Algorithm Mutations for Molecules Using a Masked Language Model. IEEE Transactions on Evolutionary Computation, 26(4), 793–799. https://doi.org/10.1109/TEVC.2022.3144045

Chandler, D. L. (2022). A dirt-cheap solution? Common clay materials may help curb methane emissions. 1.

Fuhr, A. S., & Sumpter, B. G. (2022). Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation. Frontiers in Materials, 9, 865270. https://doi.org/10.3389/fmats.2022.865270

Jordan, R. (2019). Stanford researchers outline vision for profitable climate change solution. 1.

Jordan, R. (2021). Removing methane from the atmosphere. 1.

Kim, B., Lee, S., & Kim, J. (2020a). Inverse design of porous materials using artificial neural networks. Science Advances, 6(1), eaax9324. https://doi.org/10.1126/sciadv.aax9324

Kim, B., Lee, S., & Kim, J. (2020b). Supplementary Material - Inverse design of porous materials using artificial neural networks. Science Advances, 6(1), eaax9324. https://doi.org/10.1126/sciadv.aax9324

Kim, S., Noh, J., Gu, G. H., Aspuru-Guzik, A., & Jung, Y. (2020). Generative Adversarial Networks for Crystal Structure Prediction. ACS Central Science, 6(8), 1412–1420. https://doi.org/10.1021/acscentsci.0c00426

Lackner, K. S. (2020). Practical constraints on atmospheric methane removal. Nature Sustainability, 3(5), 357–357. https://doi.org/10.1038/s41893-020-0496-7

Lee, S., Kim, B., & Kim, J. (n.d.). Supplementary Information. 30.

Lee, S., Kim, B., & Kim, J. (2019). Predicting performance limits of methane gas storage in zeolites with an artificial neural network. Journal of Materials Chemistry A, 7(6), 2709–2716. https://doi.org/10.1039/C8TA12208C

Lee, S.-Y., & Holder, G. D. (2001). Methane hydrates potential as a future energy source. Fuel Processing Technology, 71(1-3), 181–186. https://doi.org/10.1016/S0378-3820(01)00145-X

Lupo Pasini, M., Zhang, P., Temple Reeve, S., & Youl Choi, J. (2022). Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems \(^{\textrm{*}}\). Machine Learning: Science and Technology, 3(2), 025007. https://doi.org/10.1088/2632-2153/ac6a51

Schriider, K.-P., Sauer, J., Leslie, M., Catlow, C. R. A., & Thomas, J. M. (1992). Bridging hydroxyl groups in zeolitic catalysts: A computer simulation of their structure, vibrational properties and acidity in protonated faujasites (H-Y zeolites). CHEMICAL PHYSICS LETTERS, 188(3), 6.

Yoon, J. (2017). The Benefits and Downsides of Methane. 1.