Brown JS, Tseo Y, Lee MA, et al. Regularized indirect learning improves phage display ligand discovery. ChemRxiv. 2023. doi:10.26434/chemrxiv-2023-jpwvn.
Orlova Y, Ridley GKeith, Zhao F, Gómez-Bombarelli R. Expanding the Extrapolation Limits of Neural Network Force Fields using Physics-Based Data Augmentation. In: Workshop on "Machine Learning for Materials" at ICLR. Workshop on "Machine Learning for Materials" at ICLR.; 2023. Available at: https://openreview.net/forum?id=AekO4y6kiEs.
Brown JS, Mohapatra S, Lee MA, et al. Unsupervised machine learning leads to an abiotic picomolar peptide ligand. ChemRxiv. 2023. doi:10.26434/chemrxiv-2023-tws4n.
Dietschreit JCB, Diestler DJ, Gómez-Bombarelli R. Entropy and Energy Profiles of Chemical Reactions. arXiv:2304.10676. 2023. doi:10.48550/arXiv.2304.10676.
Peng J, Damewood J, Gomez-Bombarelli R. Data-Driven, Physics-Informed Descriptors of Cation Ordering in Multicomponent Oxides. arXiv:2305.01806. 2023. doi: https://doi.org/10.48550/arXiv.2305.01806.
Schwalbe-Koda D, Gómez-Bombarelli R. Generating, Managing, and Mining Big Data in Zeolite Simulations. In: AI‐Guided Design and Property Prediction for Zeolites and Nanoporous Materials. AI‐Guided Design and Property Prediction for Zeolites and Nanoporous Materials. John Wiley & Sons, Ltd; 2023:81-111. doi:https://doi.org/10.1002/9781119819783.ch4.
Damewood J, Karaguesian J, Lunger JR, et al. Representations of Materials for Machine Learning. Annual Review of Materials Research. 2023;53. doi:10.1146/annurev-matsci-080921-085947.
Bradford G, Lopez J, Ruza J, et al. Chemistry-Informed Machine Learning for Polymer Electrolyte Discovery. ACS Central Science. 2023. doi:10.1021/acscentsci.2c01123.
Wang W, Wu Z, Dietschreit JCarl Berto, Gómez-Bombarelli R. Learning Pair Potentials using Differentiable Simulations. The Journal of Chemical Physics. 2023;158:044113. doi:10.1063/5.0126475.
Tan AR, Urata S, Yamada M, Gómez-Bombarelli R. Graph theory-based structural analysis on density anomaly of silica glass. Computational Materials Science. 2023;225:112190. doi:10.1016/j.commatsci.2023.112190.
Axelrod S, Gómez-Bombarelli R. Molecular machine learning with conformer ensembles. Machine Learning: Science and Technology. 2023. doi:10.1088/2632-2153/acefa7.
Farghaly AA, Ferrandon M, Schwalbe-Koda D, et al. Machine Learning and High Throughput Synthesis Acceleration of the Discovery of Alkaline Electrolyte Oxygen Evolution Reaction Electrocatalysts. In: ECS Meeting Abstracts. ECS Meeting Abstracts. IOP Publishing; 2022. doi:10.1149/MA2022-02441673mtgabs.
Frey N, Soklaski R, Axelrod S, et al. Neural Scaling of Deep Chemical Models. ChemRxiv. 2022. doi:10.26434/chemrxiv-2022-3s512.
Dietschreit JCB, Diestler DJ, Hulm A, Ochsenfeld C, Gómez-Bombarelli R. From Free-Energy Profiles to Activation Free Energies. The Journal of Chemical Physics. 2022;157:084113. doi:10.1063/5.0102075.
Wang W, Xu M, Cai C, et al. Generative Coarse-Graining of Molecular Conformations. arXiv:2201.12176. 2022. doi:10.48550/arXiv.2201.12176.
Greenman KP, Green WH, Gómez-Bombarelli R. Multi-fidelity prediction of molecular optical peaks with deep learning. Chemical Science. 2022;13(4):1152 - 1162. doi:10.1039/D1SC05677H.
Karaguesian J, Lunger JR, Shao-Horn Y, Gómez-Bombarelli R. Crystal graph convolutional neural networks for per-site property prediction. In: Fourth Workshop on Machine Learning and the Physical Sciences at NeurIPS. Fourth Workshop on Machine Learning and the Physical Sciences at NeurIPS.; 2021. Available at: https://ml4physicalsciences.github.io/2021/files/NeurIPS_ML4PS_2021_135.pdf.
Li C, Zhang G, Mohapatra S, et al. Machine Learning Guides Peptide Nucleic Acid Flow Synthesis and Sequence Design. Advanced Science. 2021:2201988. doi:10.1002/advs.202201988.
Xu M, Wang W, Luo S, et al. An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming. In: International Conference on Machine Learning. International Conference on Machine Learning.; 2021. Available at: https://arxiv.org/abs/2105.07246.
Schwalbe-Koda D, Corma A, Román-Leshkov Y, Moliner M, Gómez-Bombarelli R. Data-Driven Design of Biselective Templates for Intergrowth Zeolites. The Journal of Physical Chemistry Letters. 2021;12:10689-10694. doi:10.1021/acs.jpclett.1c03132.
Schissel CK, Mohapatra S, Wolfe JM, et al. Deep learning to design nuclear-targeting abiotic miniproteins. Nature Chemistry. 2021. doi:10.1038/s41557-021-00766-3.
Schwalbe-Koda D, Gómez-Bombarelli R. Supramolecular Recognition in Crystalline Nanocavities Through Monte Carlo and Voronoi Network Algorithms. The Journal of Physical Chemistry C. 2021;125 (5):3009-3017. doi:10.1021/acs.jpcc.0c10108.
Gómez-Bombarelli R, Aspuru-Guzik A. Machine learning and big-data in computational chemistry. Handbook of Materials Modeling: Methods: Theory and Modeling. 2020:1939–1962.
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. Machine Learning Meets Quantum Physics. Cham: Springer International Publishing; 2020:445 - 467. doi:10.1007/978-3-030-40245-7_21.
Montáns FJ, Chinesta F, Gómez-Bombarelli R, J Kutz N. Data-driven modeling and learning in science and engineering. Comptes Rendus Mécanique. 2019;347:845–855. doi:10.1016/j.crme.2019.11.009.
Aykol M, Hummelshøj JS, Anapolsky A, et al. The Materials Research Platform: Defining the Requirements from User Stories. Matter. 2019;1(6):1433 - 1438. doi:https://doi.org/10.1016/j.matt.2019.10.024.
Wang W, Gómez-Bombarelli R. Coarse-graining auto-encoders for molecular dynamics. npj Computational Materials. 2019;5(1):125. doi:doi:10.1038/s41524-019-0261-5.
Schwalbe-Koda D, Jensen Z, Olivetti EA, Gómez-Bombarelli R. Graph similarity drives zeolite diffusionless transformations and intergrowth. Nature Materials. 2019;18:1177 - 1179. doi:https://doi.org/10.1038/s41563-019-0486-1.
Gómez-Bombarelli R, Wei JN, Duvenaud D, et al. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS Central Science. 2018;4(2):268 - 276. doi:10.1021/acscentsci.7b00572.
Gómez-Bombarelli R. Reaction: The Near Future of Artificial Intelligence in Materials Discovery. Chem. 2018;4(6):1189 - 1190. doi:10.1016/j.chempr.2018.05.021.
Gerhardt MR, Tong L, Gómez-Bombarelli R, et al. Anthraquinone Derivatives in Aqueous Flow Batteries. Advanced Energy Materials. 2017;7. doi:10.1002/aenm.201601488.
Fruchtman A, Gómez-Bombarelli R, Lovett BW, Gauger EM. Photocell optimization using dark state protection. Physical Review Letters. 2016;117. doi:10.1103/PhysRevLett.117.203603.
Gómez-Bombarelli R, Aguilera-Iparraguirre J, Hirzel TD, et al. Turbocharged molecular discovery of OLED emitters: From high-throughput quantum simulation to highly efficient TADF devices. In: Proceedings of SPIE - The International Society for Optical Engineering.Vol 9941. Proceedings of SPIE - The International Society for Optical Engineering.; 2016. doi:10.1117/12.2236966.
Lin K, Gómez-Bombarelli R, Beh ES, et al. A redox-flow battery with an alloxazine-based organic electrolyte. Nature Energy. 2016;1. doi:10.1038/nenergy.2016.102.
Aspuru-Guzik A, Adams R, Baldo M, Aguilera-Iparraguirre J, Gómez-Bombarelli R. Combinatorial design of OLED-emitting materials. In: Digest of Technical Papers - SID International Symposium.Vol 46. Digest of Technical Papers - SID International Symposium.; 2015.
Duvenaud D, Maclaurin D, Aguilera-Iparraguirre J, et al. Convolutional networks on graphs for learning molecular fingerprints. In: Advances in Neural Information Processing Systems.Vol 2015-Janua. Advances in Neural Information Processing Systems.; 2015.
Irish EK, Gómez-Bombarelli R, Lovett BW. Vibration-assisted resonance in photosynthetic excitation-energy transfer. Physical Review A - Atomic, Molecular, and Optical Physics. 2014;90. doi:10.1103/PhysRevA.90.012510.
Gómez-Bombarelli R, Calle E, Casado J. Mechanisms of lactone hydrolysis in acidic conditions. Journal of Organic Chemistry. 2013;78. doi:10.1021/jo4002596.
Gómez-Bombarelli R, Calle E, Casado J. Mechanisms of lactone hydrolysis in neutral and alkaline conditions. Journal of Organic Chemistry. 2013;78. doi:10.1021/jo400258w.
Gómez-Bombarelli R, Calle E, Casado J. DNA damage by genotoxic hydroxyhalofuranones: An in silico approach to MX. Environmental Science and Technology. 2012;46. doi:10.1021/es303105s.
González-Pérez M, Gómez-Bombarelli R, Arenas-Valgañõn J, et al. Connecting the chemical and biological reactivity of epoxides. Chemical Research in Toxicology. 2012;25. doi:10.1021/tx300389z.
Gómez-Bombarelli R, González-Pérez M, Calle E, Casado J. Reactivity of mucohalic acids in water. Water Research. 2011;45. doi:10.1016/j.watres.2010.08.040.
Céspedes-Camacho IF, Manso JA, M. Pérez-Prior T, et al. Reactivity of acrylamide as an alkylating agent: A kinetic approach. Journal of Physical Organic Chemistry. 2010;23. doi:10.1002/poc.1600.
Gómez-Bombarelli R, Palma BB, Martins C, et al. Alkylating potential of oxetanes. Chemical Research in Toxicology. 2010;23. doi:10.1021/tx100153w.
Manso JA, Pérez-Prior MT, Gómez-Bombarelli R, et al. Alkylating potential of N-phenyl-N-nitrosourea. Journal of Physical Organic Chemistry. 2009;22. doi:10.1002/poc.1456.
Larciprete MC, Dini D, Ostuni R, et al. Optical switching of a photochromic bis-phenylazo compound in PMMA films. Journal of Materials Science. 2007;42. doi:10.1007/s10853-007-1657-z.
Micó XA, Gómez-Bombarelli R, Subramanian LR, Ziegler T. Ring-opening reactions of benzotriazoles with Wittig reagents. Tetrahedron Letters. 2006;47. doi:10.1016/j.tetlet.2006.09.025.