Found 43 results
Filters: Author is Gómez-Bombarelli, Rafael  [Clear All Filters]
H. V. - T. Nguyen, Jiang, Y., Mohapatra, S., Wang, W., Barnes, J. C., Oldenhuis, N. J., Chen, K. K., Axelrod, S., Huang, Z., Chen, Q., Golder, M. R., Young, K., Suvlu, D., Shen, Y., Willard, A. P., Hore, M. J. A., Gómez-Bombarelli, R., and Johnson, J. A., Bottlebrush polymers with flexible enantiomeric side chains display differential biological properties, Nature Chemistry, vol. 14, pp. 85-93, 2022.
S. Mohapatra, An, J., and Gómez-Bombarelli, R., Chemistry-informed Macromolecule Graph Representation for Similarity Computation, Unsupervised and Supervised Learning, Machine Learning: Science and Technology, 2022.
S. Gong, Xie, T., Shao-Horn, Y., Gómez-Bombarelli, R., and Grossman, J. C., Examining graph neural networks for crystal structures: limitation on capturing periodicity, arXiv:2208.05039, 2022.
S. Axelrod, Shakhnovich, E., and Gómez-Bombarelli, R., Excited state non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential, Nature Communications, vol. 13, p. 3440, 2022.
J. C. B. Dietschreit, Diestler, D. J., Hulm, A., Ochsenfeld, C., and Gómez-Bombarelli, R., From Free-Energy Profiles to Activation Free Energies, The Journal of Chemical Physics, vol. 157, p. 084113, 2022.
W. Wang, Xu, M., Cai, C., Miller, B. Kurt, Smidt, T., Wang, Y., Tang, J., and Gómez-Bombarelli, R., Generative Coarse-Graining of Molecular Conformations, arXiv:2201.12176, 2022.
S. Axelrod and Gómez-Bombarelli, R., GEOM: Energy-annotated molecular conformations for property prediction and molecular generation, Scientific Data, vol. 9, p. 185, 2022.
J. Peng, Schwalbe-Koda, D., Akkiraju, K., Xie, T., Giordano, L., Yu, Y., C. Eom, J., Lunger, J. R., Zheng, D. J., Rao, R. R., Muy, S., Grossman, J. C., Reuter, K., Gómez-Bombarelli, R., and Shao-Horn, Y., Human- and machine-centred designs of molecules and materials for sustainability and decarbonization, Nature Reviews Materials, 2022.
S. Axelrod, Schwalbe-Koda, D., Mohapatra, S., Damewood, J., Greenman, K. P., and Gómez-Bombarelli, R., Learning Matter: Materials Design with Machine Learning and Atomistic Simulations, Accounts of Materials Research, 2022.
W. Wang, Wu, Z., and Gómez-Bombarelli, R., Learning Pair Potentials using Differentiable Simulations, arXiv:2209.07679, 2022.
K. P. Greenman, Green, W. H., and Gómez-Bombarelli, R., Multi-fidelity prediction of molecular optical peaks with deep learning, Chemical Science, vol. 13(4), pp. 1152 - 1162, 2022.
N. Frey, Soklaski, R., Axelrod, S., Samsi, S., Gómez-Bombarelli, R., Coley, C., and Gadepally, V., Neural Scaling of Deep Chemical Models, ChemRxiv, 2022.
D. Schwalbe-Koda, Santiago-Reyes, O. A., Corma, A., Román-Leshkov, Y., Moliner, M., and Gómez-Bombarelli, R., Repurposing Templates for Zeolite Synthesis from Simulations and Data Mining, Chemistry of Materials, 2022.
J. Damewood, Schwalbe-Koda, D., and Gómez-Bombarelli, R., Sampling Lattices in Semi-Grand Canonical Ensemble with Autoregressive Machine Learning, npj Computational Materials, vol. 8, p. 61, 2022.
S. Urata, Nakamura, N., Tada, T., Tan, A. Rui, Gómez-Bombarelli, R., and Hosono, H., Suppression of Rayleigh Scattering in Silica Glass by Codoping Boron and Fluorine: Molecular Dynamics Simulations with Force-Matching and Neural Network Potentials, The Journal of Physical Chemistry C, vol. 126(4), pp. 2264–2275, 2022.
E. Bello-Jurado, Schwalbe-Koda, D., Nero, M., Paris, C., Uusimäki, T., Román-Leshkov, Y., Corma, A., Willhammar, T., Gómez-Bombarelli, R., and Moliner, M., Tunable CHA/AEI Zeolite Intergrowths with A Priori Biselective Organic Structure-Directing Agents: Controlling Enrichment and Implications for Selective Catalytic Reduction of NOx, Angewandte Chemie International Edition, 2022.
T. Xie, France-Lanord, A., Wang, Y., Lopez, J., Stolberg, M. Austin, Hill, M., Leverick, G. Michael, Gómez-Bombarelli, R., Johnson, J. A., Shao-Horn, Y., and Grossman, J. C., Accelerating the screening of amorphous polymer electrolytes by learning to reduce random and systematic errors in molecular dynamics simulations, arXiv:2101.05339, 2021.
D. Schwalbe-Koda and Gómez-Bombarelli, R., Benchmarking binding energy calculations for organic structure-directing agents in pure-silica zeolites, The Journal of Chemical Physics, vol. 154, p. 174109, 2021.
D. Schwalbe-Koda, Corma, A., Román-Leshkov, Y., Moliner, M., and Gómez-Bombarelli, R., Data-Driven Design of Biselective Templates for Intergrowth Zeolites, The Journal of Physical Chemistry Letters, vol. 12, pp. 10689-10694, 2021.
E. M. López-Vidal, Schissel, C. K., Mohapatra, S., Bellovoda, K., Wu, C. - L., Wood, J. A., Malmberg, A. B., Loas, A., Gómez-Bombarelli, R., and Pentelute, B. L., Deep Learning Enables Discovery of a Short Nuclear Targeting Peptide for Efficient Delivery of Antisense Oligomers, JACS Au, vol. 1(11), pp. 2009–2020, 2021.
C. K. Schissel, Mohapatra, S., Wolfe, J. M., Fadzen, C. M., Bellovoda, K., Wu, C. - L., Wood, J. A., Malmberg, A. B., Loas, A., Gómez-Bombarelli, R., and Pentelute, B. L., Deep learning to design nuclear-targeting abiotic miniproteins, Nature Chemistry, 2021.
Z. Jensen, Kwon, S., Schwalbe-Koda, D., Paris, C., Gómez-Bombarelli, R., Román-Leshkov, Y., Corma, A., Moliner, M., and Olivetti, E. A., Discovering relationships between OSDAs and zeolites through data mining and generative neural networks, ACS Central Science, vol. 7, pp. 858–867, 2021.
M. Xu, Wang, W., Luo, S., Shi, C., Bengio, Y., Gómez-Bombarelli, R., and Tang, J., An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming, in International Conference on Machine Learning, 2021.
A. Rui Tan, Urata, S., Yamada, M., and Gómez-Bombarelli, R., Graph theory-based structural analysis on density anomaly of silica glass, arXiv:2111.07452, 2021.
C. Li, Zhang, G., Mohapatra, S., Callahan, A., Loas, A., Gómez-Bombarelli, R., and Pentelute, B., Machine Learning Guides Peptide Nucleic Acid Flow Synthesis and Sequence Design, ChemRxiv, 2021.
D. Schwalbe-Koda and Gómez-Bombarelli, R., Supramolecular Recognition in Crystalline Nanocavities Through Monte Carlo and Voronoi Network Algorithms, The Journal of Physical Chemistry C, vol. 125 (5), pp. 3009-3017, 2021.
S. Jun Ang, Wang, W., Schwalbe-Koda, D., Axelrod, S., and Gómez-Bombarelli, R., Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces, Chem, vol. 7(3), pp. 738-51, 2020.
W. Wang, Yang, T., Harris, W. Hunt, and Gómez-Bombarelli, R., Active Learning and Neural Network Potentials Accelerate Molecular Screening of Ether-based Solvate Ionic Liquids, Chemical Communications, 2020.
W. Wang, Axelrod, S., and Gómez-Bombarelli, R., Differentiable Molecular Simulations for Control and Learning, arXiv:2003.00868, 2020.
D. Schwalbe-Koda and Gómez-Bombarelli, R., Generative Models for Automatic Chemical Design, in Machine Learning Meets Quantum Physics, K. T. Schütt, Chmiela, S., O. von Lilienfeld, A., Tkatchenko, A., Tsuda, K., and Müller, K. - R. Cham: Springer International Publishing, 2020, pp. 445 - 467.
R. Gómez-Bombarelli and Aspuru-Guzik, A., Machine learning and big-data in computational chemistry, Handbook of Materials Modeling: Methods: Theory and Modeling, pp. 1939–1962, 2020.
S. Axelrod and Gómez-Bombarelli, R., Molecular machine learning with conformer ensembles, arXiv:2012.08452, 2020.
B. Qiao, Mohapatra, S., Lopez, J., Leverick, G., Tatara, R., Shibuya, Y., Jiang, Y., France-Lanord, A., Grossman, J. C., Gómez-Bombarelli, R., Johnson, J., and Shao-Horn, Y., Quantitative Mapping of Molecular Substituents to Macroscopic Properties Enables Predictive Design of Oligoethyleneglycol-Based Lithium Electrolytes, ACS Central Science, 2020.
J. Ruza, Wang, W., Schwalbe-Koda, D., Axelrod, S., Harris, W. Hunt, and Gómez-Bombarelli, R., Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks, The Journal of Chemical Physics, vol. 153, p. 164501, 2020.
W. Wang and Gómez-Bombarelli, R., Coarse-graining auto-encoders for molecular dynamics, npj Computational Materials, vol. 5, no. 1, p. 125, 2019.
F. J. Montáns, Chinesta, F., Gómez-Bombarelli, R., and J Kutz, N., Complex algorithms for data-driven model learning in science and engineering. 2019.
R. Gómez-Bombarelli and Aspuru-Guzik, A., Computational discovery of organic LED materials, Comput. Mater. Disc, pp. 423–446, 2019.
F. J. Montáns, Chinesta, F., Gómez-Bombarelli, R., and J Kutz, N., Data-driven modeling and learning in science and engineering, Comptes Rendus Mécanique, vol. 347, pp. 845–855, 2019.
D. Schwalbe-Koda, Jensen, Z., Olivetti, E., and Gómez-Bombarelli, R., Graph similarity drives zeolite diffusionless transformations and intergrowth, Nature Materials, vol. 18, pp. 1177 - 1179, 2019.
D. P. Tabor, Gómez-Bombarelli, R., Tong, L., Gordon, R. G., Aziz, M. J., and Aspuru-Guzik, A., Mapping the frontiers of quinone stability in aqueous media: implications for organic aqueous redox flow batteries, J. Mater. Chem. A, vol. 7, pp. 12833-12841, 2019.
M. Aykol, Hummelshøj, J. S., Anapolsky, A., Aoyagi, K., Bazant, M. Z., Bligaard, T., Braatz, R. D., Broderick, S., Cogswell, D., Dagdelen, J., Drisdell, W., Garcia, E., Garikipati, K., Gavini, V., Gent, W. E., Giordano, L., Gomes, C. P., Gómez-Bombarelli, R., Gopal, C. Balaji, Gregoire, J. M., Grossman, J. C., Herring, P., Hung, L., Jaramillo, T. F., King, L., Kwon, H. - K., Maekawa, R., Minor, A. M., Montoya, J. H., Mueller, T., Ophus, C., Rajan, K., Ramprasad, R., Rohr, B., Schweigert, D., Shao-Horn, Y., Suga, Y., Suram, S. K., Viswanathan, V., Whitacre, J. F., Willard, A. P., Wodo, O., Wolverton, C., and Storey, B. D., The Materials Research Platform: Defining the Requirements from User Stories, Matter, vol. 1, no. 6, pp. 1433 - 1438, 2019.