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ORCIDRafael Gómez-Bombarelli
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- affiliation: Massachusetts Institute of Technology, Department of Materials Science and Engineering, Cambridge, MA, USA
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2020 – today
- 2024
[i26]Soojung Yang, Juno Nam, Johannes C. B. Dietschreit, Rafael Gómez-Bombarelli
:
Learning Collective Variables for Protein Folding with Labeled Data Augmentation through Geodesic Interpolation. CoRR abs/2402.01542 (2024)- [i25]Aik Rui Tan, Johannes C. B. Dietschreit, Rafael Gómez-Bombarelli:
Enhanced sampling of robust molecular datasets with uncertainty-based collective variables. CoRR abs/2402.03753 (2024) - [i24]Juno Nam, Rafael Gómez-Bombarelli:
Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials. CoRR abs/2404.10746 (2024) - [i23]Jiayu Peng, James K. Damewood, Jessica Karaguesian, Jaclyn R. Lunger, Rafael Gómez-Bombarelli:
Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks. CoRR abs/2409.13851 (2024) - [i22]Sulin Liu, Juno Nam, Andrew Campbell, Hannes Stärk, Yilun Xu, Tommi S. Jaakkola, Rafael Gómez-Bombarelli:
Think While You Generate: Discrete Diffusion with Planned Denoising. CoRR abs/2410.06264 (2024) - 2023
- [j8]Simon Axelrod, Eugene Shakhnovich, Rafael Gómez-Bombarelli:
Mapping the Space of Photoswitchable Ligands and Photodruggable Proteins with Computational Modeling. J. Chem. Inf. Model. 63(18): 5794-5802 (2023) - [j7]Simon Axelrod, Rafael Gómez-Bombarelli:
Molecular machine learning with conformer ensembles. Mach. Learn. Sci. Technol. 4(3): 35025 (2023) - [j6]Nathan C. Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi, Rafael Gómez-Bombarelli, Connor W. Coley, Vijay Gadepally:
Neural scaling of deep chemical models. Nat. Mac. Intell. 5(11): 1297-1305 (2023) - [j5]Xiaochen Du, James K. Damewood, Jaclyn R. Lunger, Reisel Millan, Bilge Yildiz, Lin Li, Rafael Gómez-Bombarelli:
Machine-learning-accelerated simulations to enable automatic surface reconstruction. Nat. Comput. Sci. 3(12): 1034-1044 (2023) - [j4]Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gómez-Bombarelli, Tommi S. Jaakkola:
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations. Trans. Mach. Learn. Res. 2023 (2023) - [c7]Martin Sípka, Johannes C. B. Dietschreit, Lukás Grajciar, Rafael Gómez-Bombarelli:
Differentiable Simulations for Enhanced Sampling of Rare Events. ICML 2023: 31990-32007 - [c6]Soojung Yang, Rafael Gómez-Bombarelli:
Chemically Transferable Generative Backmapping of Coarse-Grained Proteins. ICML 2023: 39277-39298 - [i21]Martin Sípka, Johannes C. B. Dietschreit, Lukás Grajciar, Rafael Gómez-Bombarelli:
Differentiable Simulations for Enhanced Sampling of Rare Events. CoRR abs/2301.03480 (2023) - [i20]Soojung Yang, Rafael Gómez-Bombarelli:
Chemically Transferable Generative Backmapping of Coarse-Grained Proteins. CoRR abs/2303.01569 (2023) - [i19]Akshay Subramanian, Kevin P. Greenman, Alexis Gervaix, Tzuhsiung Yang, Rafael Gómez-Bombarelli:
Automated patent extraction powers generative modeling in focused chemical spaces. CoRR abs/2303.08272 (2023) - [i18]Aik Rui Tan, Shingo Urata, Samuel Goldman, Johannes C. B. Dietschreit, Rafael Gómez-Bombarelli:
Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles. CoRR abs/2305.01754 (2023) - [i17]Xiaochen Du, James K. Damewood, Jaclyn R. Lunger, Reisel Millan, Bilge Yildiz, Lin Li, Rafael Gómez-Bombarelli:
Machine-learning-accelerated simulations enable heuristic-free surface reconstruction. CoRR abs/2305.07251 (2023) - 2022
- [j3]Somesh Mohapatra, Joyce An, Rafael Gómez-Bombarelli:
Chemistry-informed macromolecule graph representation for similarity computation, unsupervised and supervised learning. Mach. Learn. Sci. Technol. 3(1): 15028 (2022) - [c5]Shomik Verma, Shivam Kajale, Rafael Gómez-Bombarelli:
Machine learning for accurate and fast bandgap prediction of solid-state materials. HPEC 2022: 1-2 - [c4]Wujie Wang, Minkai Xu, Chen Cai, Benjamin Kurt Miller, Tess E. Smidt, Yusu Wang, Jian Tang, Rafael Gómez-Bombarelli:
Generative Coarse-Graining of Molecular Conformations. ICML 2022: 23213-23236 - [c3]Nathan C. Frey, Dan Zhao, Simon Axelrod, Michael Jones, David Bestor, Vijay Gadepally, Rafael Gómez-Bombarelli, Siddharth Samsi:
Energy-aware neural architecture selection and hyperparameter optimization. IPDPS Workshops 2022: 732-741 - [i16]Wujie Wang, Minkai Xu, Chen Cai, Benjamin Kurt Miller, Tess E. Smidt, Yusu Wang, Jian Tang, Rafael Gómez-Bombarelli:
Generative Coarse-Graining of Molecular Conformations. CoRR abs/2201.12176 (2022) - [i15]Simon Axelrod, Eugene Shakhnovich, Rafael Gómez-Bombarelli:
Thermal half-lives of azobenzene derivatives: virtual screening based on intersystem crossing using a machine learning potential. CoRR abs/2207.11592 (2022) - [i14]Wujie Wang, Zhenghao Wu, Rafael Gómez-Bombarelli:
Learning Pair Potentials using Differentiable Simulations. CoRR abs/2209.07679 (2022) - [i13]Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gómez-Bombarelli, Tommi S. Jaakkola:
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations. CoRR abs/2210.07237 (2022) - 2021
- [c2]Minkai Xu, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, Rafael Gómez-Bombarelli, Jian Tang:
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming. ICML 2021: 11537-11547 - [i12]Tian Xie, Arthur France-Lanord, Yanming Wang, Jeffrey Lopez, Michael Austin Stolberg, Megan Hill, Graham Michael Leverick, Rafael Gómez-Bombarelli, Jeremiah A. Johnson, Yang Shao-Horn, Jeffrey C. Grossman:
Accelerating the screening of amorphous polymer electrolytes by learning to reduce random and systematic errors in molecular dynamics simulations. CoRR abs/2101.05339 (2021) - [i11]Daniel Schwalbe-Koda, Aik Rui Tan, Rafael Gómez-Bombarelli:
Adversarial Attacks on Uncertainty Enable Active Learning for Neural Network Potentials. CoRR abs/2101.11588 (2021) - [i10]Somesh Mohapatra, Joyce An, Rafael Gómez-Bombarelli:
Chemistry-informed Macromolecule Graph Representation for Similarity Computation and Supervised Learning. CoRR abs/2103.02565 (2021) - [i9]Minkai Xu, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, Rafael Gómez-Bombarelli, Jian Tang:
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming. CoRR abs/2105.07246 (2021) - [i8]Simon Axelrod, Eugene Shakhnovich, Rafael Gómez-Bombarelli:
Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential. CoRR abs/2108.04879 (2021) - 2020
- [j2]Somesh Mohapatra, Tzuhsiung Yang, Rafael Gómez-Bombarelli:
Reusability report: Designing organic photoelectronic molecules with descriptor conditional recurrent neural networks. Nat. Mach. Intell. 2(12): 749-752 (2020) - [i7]Wujie Wang, Simon Axelrod, Rafael Gómez-Bombarelli:
Differentiable Molecular Simulations for Control and Learning. CoRR abs/2003.00868 (2020) - [i6]Simon Axelrod, Rafael Gómez-Bombarelli:
GEOM: Energy-annotated molecular conformations for property prediction and molecular generation. CoRR abs/2006.05531 (2020) - [i5]Simon Axelrod, Rafael Gómez-Bombarelli:
Molecular machine learning with conformer ensembles. CoRR abs/2012.08452 (2020)
2010 – 2019
- 2019
- [j1]Francisco Javier Montáns, Francisco Chinesta, Rafael Gómez-Bombarelli, J. Nathan Kutz:
Complex Algorithms for Data-Driven Model Learning in Science and Engineering. Complex. 2019: 5040637:1-5040637:3 (2019) - [i4]Daniel Schwalbe-Koda, Rafael Gómez-Bombarelli:
Generative Models for Automatic Chemical Design. CoRR abs/1907.01632 (2019) - 2018
- [i3]Wujie Wang, Rafael Gómez-Bombarelli:
Variational Coarse-Graining for Molecular Dynamics. CoRR abs/1812.02706 (2018) - 2016
- [i2]Rafael Gómez-Bombarelli, David Duvenaud, José Miguel Hernández-Lobato, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, Alán Aspuru-Guzik:
Automatic chemical design using a data-driven continuous representation of molecules. CoRR abs/1610.02415 (2016) - 2015
- [c1]David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams:
Convolutional Networks on Graphs for Learning Molecular Fingerprints. NIPS 2015: 2224-2232 - [i1]David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams:
Convolutional Networks on Graphs for Learning Molecular Fingerprints. CoRR abs/1509.09292 (2015)
Coauthor Index
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