Rafael Gómez-Bombarelli

Orcid: 0000-0002-9495-8599

Affiliations:
  • Massachusetts Institute of Technology, Department of Materials Science and Engineering, Cambridge, MA, USA


According to our database1, Rafael Gómez-Bombarelli authored at least 38 papers between 2015 and 2024.

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Bibliography

2024
Univariate Conditional Variational Autoencoder for Morphogenic Patterns Design in Frontal Polymerization-Based Manufacturing.
CoRR, 2024

Symmetry-Constrained Generation of Diverse Low-Bandgap Molecules with Monte Carlo Tree Search.
CoRR, 2024

Efficient Generation of Molecular Clusters with Dual-Scale Equivariant Flow Matching.
CoRR, 2024

Think While You Generate: Discrete Diffusion with Planned Denoising.
CoRR, 2024

Flow Matching for Accelerated Simulation of Atomic Transport in Materials.
CoRR, 2024

Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks.
CoRR, 2024

Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials.
CoRR, 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
Neural scaling of deep chemical models.
Nat. Mac. Intell., October, 2023

Molecular machine learning with conformer ensembles.
Mach. Learn. Sci. Technol., September, 2023

Mapping the Space of Photoswitchable Ligands and Photodruggable Proteins with Computational Modeling.
J. Chem. Inf. Model., September, 2023

Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations.
Trans. Mach. Learn. Res., 2023

Machine-learning-accelerated simulations to enable automatic surface reconstruction.
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

Automated patent extraction powers generative modeling in focused chemical spaces.
CoRR, 2023

Chemically Transferable Generative Backmapping of Coarse-Grained Proteins.
Proceedings of the International Conference on Machine Learning, 2023

Differentiable Simulations for Enhanced Sampling of Rare Events.
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

Learning Pair Potentials using Differentiable Simulations.
CoRR, 2022

Thermal half-lives of azobenzene derivatives: virtual screening based on intersystem crossing using a machine learning potential.
CoRR, 2022

Energy-aware neural architecture selection and hyperparameter optimization.
Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2022

Generative Coarse-Graining of Molecular Conformations.
Proceedings of the International Conference on Machine Learning, 2022

Machine learning for accurate and fast bandgap prediction of solid-state materials.
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

Differentiable Molecular Simulations for Control and Learning.
CoRR, 2020

2019
Generative Models for Automatic Chemical Design.
CoRR, 2019

Complex Algorithms for Data-Driven Model Learning in Science and Engineering.
Complex., 2019

2018
Variational Coarse-Graining for Molecular Dynamics.
CoRR, 2018

2016
Automatic chemical design using a data-driven continuous representation of molecules.
CoRR, 2016

2015
Convolutional Networks on Graphs for Learning Molecular Fingerprints.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015


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