Publications

Found 13 results
Filters: Author is Gómez-Bombarelli, Rafael  [Clear All Filters]
2023
P. Ferri, Li, C., Schwalbe-Koda, D., Xie, M., Moliner, M., Gómez-Bombarelli, R., Boronat, M., and Corma, A., “Approaching enzymatic catalysis with zeolites or how to select one reaction mechanism competing with others”, Nature Communications, vol. 14, p. 2878, 2023.
J. R. Lunger, Karaguesian, J., Chun, H., Peng, J., Tseo, Y., Shan, C. Hsuan, Han, B., Shao-Horn, Y., and Gómez-Bombarelli, R., “Atom-by-atom design of metal oxide catalysts for the oxygen evolution reaction with machine learning”, arXiv:2305.19930, 2023.
R. Millan, Bello-Jurado, E., Moliner, M., Boronat, M., and Gómez-Bombarelli, R., “Effect of framework composition and NH3 on the diffusion of Cu+ in Cu-CHA catalysts predicted by machine-learning accelerated molecular dynamics”, arXiv:2305.12896, 2023.
J. C. B. Dietschreit, Diestler, D. J., and Gómez-Bombarelli, R., “Entropy and Energy Profiles of Chemical Reactions”, arXiv:2304.10676, 2023.
Y. Orlova, Ridley, G. Keith, Zhao, F., and Gómez-Bombarelli, R., “Expanding the Extrapolation Limits of Neural Network Force Fields using Physics-Based Data Augmentation”, in Workshop on "Machine Learning for Materials" at ICLR, 2023.
M. Stolberg, Paren, B., Leon, P., Brown, C., Winter, G., Gordiz, K., Concellón, A., Gómez-Bombarelli, R., Shao-Horn, Y., and Johnson, J., “Lamellar ionenes with highly dissociative, anionic channels provide lower barriers for cation transport”, J. Am. Chem. Soc., 2023.
S. Roy, Dürholt, J. P., Asche, T. S., Zipoli, F., and Gómez-Bombarelli, R., “Learning a reactive potential for silica-water through uncertainty attribution”, arXiv:2307.01705, 2023.
X. Du, Damewood, J. K., Lunger, J. R., Millan, R., Yildiz, B., Li, L., and Gómez-Bombarelli, R., “Machine-learning-accelerated simulations enable heuristic-free surface reconstruction”, arXiv:2305.07251, 2023.
Y. AlFaraj, Mohapatra, S., Shieh, P., Husted, K., Ivanoff, D., Lloyd, E., Cooper, J., Dai, Y., Singhal, A., Moore, J., Sottos, N., Gómez-Bombarelli, R., and Johnson, J. A., “A Model Ensemble Approach Enables Data-Driven Property Prediction for Chemically Deconstructable Thermosets in the Low Data Regime”, ChemRxiv, 2023.
J. S. Brown, Tseo, Y., Lee, M. A., Wong, J. Y. - K., Yang, S., Cho, Y., Kim, C. Rin, Loas, A., Derda, R., Gómez-Bombarelli, R., and Pentelute, B. L., “Regularized indirect learning improves phage display ligand discovery”, ChemRxiv, 2023.
A. Rui Tan, Urata, S., Goldman, S., Dietschreit, J. C. B., and Gómez-Bombarelli, R., “Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles”, arXiv:2305.01754, 2023.
J. S. Brown, Mohapatra, S., Lee, M. A., Misteli, R., Tseo, Y., Grob, N. M., Quartararo, A. J., Loas, A., Gómez-Bombarelli, R., and Pentelute, B. L., “Unsupervised machine learning leads to an abiotic picomolar peptide ligand”, ChemRxiv, 2023.
2021
J. Karaguesian, Lunger, J. R., Shao-Horn, Y., and Gómez-Bombarelli, R., “Crystal graph convolutional neural networks for per-site property prediction”, in Fourth Workshop on Machine Learning and the Physical Sciences at NeurIPS, 2021.