Biblio

Found 21 results
Filters: Author is Gómez-Bombarelli, Rafael  [Clear All Filters]
2020
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, 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, 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.
S. Axelrod and Gómez-Bombarelli, R., GEOM: Energy-annotated molecular conformations for property prediction and molecular generation, arXiv preprint arXiv:2006.05531, 2020.
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., Interpretable Deep Learning for De Novo Design of Cell-Penetrating Abiotic Polymers, bioRxiv, 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, 2020.