Rafael Gómez-Bombarelli
Orcid: 0000-0002-9495-8599Affiliations:
- Massachusetts Institute of Technology, Department of Materials Science and Engineering, Cambridge, MA, USA
According to our database1,
Rafael Gómez-Bombarelli
authored at least 31 papers
between 2015 and 2024.
Collaborative distances:
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Bibliography
2024
Enhanced sampling of robust molecular datasets with uncertainty-based collective variables.
CoRR, 2024
Learning Collective Variables for Protein Folding with Labeled Data Augmentation through Geodesic Interpolation.
CoRR, 2024
2023
Mach. Learn. Sci. Technol., September, 2023
Mapping the Space of Photoswitchable Ligands and Photodruggable Proteins with Computational Modeling.
J. Chem. Inf. Model., September, 2023
Nat. Comput. Sci., 2023
Machine-learning-accelerated simulations enable heuristic-free surface reconstruction.
CoRR, 2023
Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles.
CoRR, 2023
CoRR, 2023
Proceedings of the International Conference on Machine Learning, 2023
Proceedings of the International Conference on Machine Learning, 2023
2022
Chemistry-informed macromolecule graph representation for similarity computation, unsupervised and supervised learning.
Mach. Learn. Sci. Technol., 2022
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations.
CoRR, 2022
Thermal half-lives of azobenzene derivatives: virtual screening based on intersystem crossing using a machine learning potential.
CoRR, 2022
Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2022
Proceedings of the International Conference on Machine Learning, 2022
Proceedings of the IEEE High Performance Extreme Computing Conference, 2022
2021
Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential.
CoRR, 2021
Chemistry-informed Macromolecule Graph Representation for Similarity Computation and Supervised Learning.
CoRR, 2021
Adversarial Attacks on Uncertainty Enable Active Learning for Neural Network Potentials.
CoRR, 2021
Accelerating the screening of amorphous polymer electrolytes by learning to reduce random and systematic errors in molecular dynamics simulations.
CoRR, 2021
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming.
Proceedings of the 38th International Conference on Machine Learning, 2021
2020
Reusability report: Designing organic photoelectronic molecules with descriptor conditional recurrent neural networks.
Nat. Mach. Intell., 2020
GEOM: Energy-annotated molecular conformations for property prediction and molecular generation.
CoRR, 2020
2019
Complex., 2019
2018
2016
Automatic chemical design using a data-driven continuous representation of molecules.
CoRR, 2016
2015
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015