2021

Greenman KP, Green WH, Gómez-Bombarelli R. Multi-fidelity prediction of molecular optical peaks with deep learning. ChemRxiv [Internet]. 2021;. https://chemrxiv.org/engage/chemrxiv/article-details/6167a17d8b620d1a974ecd69
Schwalbe-Koda D, Corma A, Román-Leshkov Y, Moliner M, Gómez-Bombarelli R. Data-driven design of bi-selective OSDAs for intergrowth zeolites. ChemRxiv. 2021;.
Schwalbe-Koda D, Kwon S, Paris C, Bello-Jurado E, Jensen Z, Olivetti E, et al.. A priori control of zeolite phase competition and intergrowth with high-throughput simulations. Science [Internet]. 2021;:eabh3350. https://www.science.org/doi/abs/10.1126/science.abh3350
Schwalbe-Koda D, Tan ARui, Gomez-Bombarelli R. Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks. Nature Communications [Internet]. 2021;12:5104. https://www.nature.com/articles/s41467-021-25342-8
Axelrod S, Shakhnovich E, Gómez-Bombarelli R. Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential. arxiv:2108.04879 [Internet]. 2021;. https://arxiv.org/abs/2108.04879
Schissel CK, Mohapatra S, Wolfe JM, Fadzen CM, Bellovoda K, Wu C-L, et al.. Deep learning to design nuclear-targeting abiotic miniproteins. Nature Chemistry [Internet]. 2021;. https://doi.org/10.1038/s41557-021-00766-3
Schwalbe-Koda D, Gómez-Bombarelli R. Benchmarking binding energy calculations for organic structure-directing agents in pure-silica zeolites. The Journal of Chemical Physics [Internet]. 2021;154:174109. https://doi.org/10.1063/5.0044927
Mohapatra S, An J, Gómez-Bombarelli R. Chemistry-informed Macromolecule Graph Representation for Similarity Computation and Supervised Learning. arXiv: 2103.02565 [Internet]. 2021;:1–25. http://arxiv.org/abs/2103.02565
Schwalbe-Koda D, Gómez-Bombarelli R. Supramolecular Recognition in Crystalline Nanocavities Through Monte Carlo and Voronoi Network Algorithms. The Journal of Physical Chemistry C [Internet]. 2021;125 (5):3009-3017. https://pubs.acs.org/doi/10.1021/acs.jpcc.0c10108

2020

Schwalbe-Koda D, Gómez-Bombarelli R. Generative Models for Automatic Chemical Design. In: Schütt KT, Chmiela S, O. von Lilienfeld A, Tkatchenko A, Tsuda K, Müller K-R. Machine Learning Meets Quantum Physics [Internet]. Cham: Springer International Publishing; 2020. pp. 445 - 467. https://doi.org/10.1007/978-3-030-40245-7_21
Axelrod S, Gómez-Bombarelli R. Molecular machine learning with conformer ensembles. arXiv:2012.08452 [Internet]. 2020;. https://arxiv.org/pdf/2012.08452.pdf
Mohapatra S, Yang T, Gomez-Bombarelli R. Reusability report: Designing organic photoelectronic molecules with descriptor conditional recurrent neural networks. Nature Machine Intelligence [Internet]. 2020;2:749–752. https://www.nature.com/articles/s42256-020-00268-w
Mohapatra S, Hartrampf N, Poskus M, Loas A. Deep Learning for Prediction and Optimization of Fast-Flow Peptide Synthesis. [Internet]. 2020;. https://chemrxiv.org/articles/preprint/Deep_Learning_for_Prediction_and_Optimization_of_Fast-Flow_Peptide_Synthesis/12649343
Wang W, Yang T, Harris WHunt, Gómez-Bombarelli R. Active Learning and Neural Network Potentials Accelerate Molecular Screening of Ether-based Solvate Ionic Liquids. Chemical Communications [Internet]. 2020;. http://pubs.rsc.org/en/Content/ArticleLanding/2020/CC/D0CC03512B
Schissel CK, Mohapatra S, Wolfe JM, Fadzen CM, Bellovoda K, Wu C-L, et al.. Interpretable Deep Learning for De Novo Design of Cell-Penetrating Abiotic Polymers. bioRxiv [Internet]. 2020;. https://www.biorxiv.org/content/10.1101/2020.04.10.036566v1
Axelrod S, Gómez-Bombarelli R. GEOM: Energy-annotated molecular conformations for property prediction and molecular generation. arXiv preprint arXiv:2006.05531 [Internet]. 2020;. https://arxiv.org/pdf/2006.05531.pdf
Qiao B, Mohapatra S, Lopez J, Leverick G, Tatara R, Shibuya Y, et al.. Quantitative Mapping of Molecular Substituents to Macroscopic Properties Enables Predictive Design of Oligoethyleneglycol-Based Lithium Electrolytes. ACS Central Science [Internet]. 2020;. https://doi.org/10.1021/acscentsci.0c00475
Ang SJun, Wang W, Schwalbe-Koda D, Axelrod S, Gómez-Bombarelli R. Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces. [Internet]. 2020;. https://chemrxiv.org/articles/Active_Learning_Accelerates_Ab_Initio_Molecular_Dynamics_on_Pericyclic_Reactive_Energy_Surfaces/11910948
Wang W, Axelrod S, Gómez-Bombarelli R. Differentiable Molecular Simulations for Control and Learning. [Internet]. 2020;. http://arxiv.org/abs/2003.00868
Ruza J, Wang W, Schwalbe-Koda D, Axelrod S, Harris WHunt, Gómez-Bombarelli R. Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks [Internet]. 2020. https://arxiv.org/abs/2007.14144

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