Fast evaluation of the adsorption energy of organic molecules on … – Nature.com

Nrskov, J. K., Bligaard, T., Rossmeisl, J. & Christensen, C. H. Towards the computational design of solid catalysts. Nat. Chem. 1, 3746 (2009).

Article Google Scholar

Zhong, M. et al. Accelerated discovery of CO2 electrocatalysts using active machine learning. Nature 581, 178183 (2020).

Article Google Scholar

Ma, X., Li, Z., Achenie, L. E. K. & Xin, H. Machine-learning-augmented chemisorption model for CO2 electroreduction catalyst screening. J. Phys. Chem. Lett. 6, 35283533 (2015).

Article Google Scholar

Cohen, N. & Benson, S. W. Estimation of heats of formation of organic compounds by additivity methods. Chem. Rev. 93, 24192438 (1993).

Article Google Scholar

Eigenmann, H. K., Golden, D. M. & Benson, S. W. Revised group additivity parameters for the enthalpies of formation of oxygen-containing organic compounds. J. Phys. Chem. 77, 16871691 (1973).

Article Google Scholar

Benson, S. W. & Buss, J. H. Additivity rules for the estimation of molecular properties. thermodynamic properties. J. Chem. Phys. 29, 546572 (1958).

Article Google Scholar

Benson, S. W. IIIBond energies. J. Chem. Educ. 42, 502 (1965).

Article Google Scholar

Benson, S. W. et al. Additivity rules for the estimation of thermochemical properties. Chem. Rev. 69, 279324 (1969).

Article Google Scholar

Sabbe, M. K. et al. Group additive values for the gas phase standard enthalpy of formation of hydrocarbons and hydrocarbon radicals. J. Phys. Chem. A 109, 74667480 (2005).

Article Google Scholar

Shustorovich, E. The bond-order conservation approach to chemisorption and heterogeneous catalysis: applications and implications. Adv. Catal. 37, 101163 (1990).

Google Scholar

Garca-Muelas, R. & Lpez, N. Collective descriptors for the adsorption of sugar alcohols on Pt and Pd(111). J. Phys. Chem. C 118, 1753117537 (2014).

Article Google Scholar

Garca-Muelas, R. & Lpez, N. Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals. Nat. Commun. 10, 4687 (2019).

Article Google Scholar

Salciccioli, M., Chen, Y. & Vlachos, D. G. Density functional theory-derived group additivity and linear scaling methods for prediction of oxygenate stability on metal catalysts: adsorption of open-ring alcohol and polyol dehydrogenation intermediates on Pt-based metals. J. Phys. Chem. C 114, 2015520166 (2010).

Article Google Scholar

Wittreich, G. R. & Vlachos, D. G. Python group additivity (pGrAdd) software for estimating species thermochemical properties. Comput. Phys. Commun. 273, 108277 (2022).

Article Google Scholar

Gu, G. H. et al. Group additivity for aqueous phase thermochemical properties of alcohols on Pt(111). J. Phys. Chem. C 121, 2151021519 (2017).

Article Google Scholar

Esterhuizen, J. A., Goldsmith, B. R. & Linic, S. Theory-guided machine learning finds geometric structureproperty relationships for chemisorption on subsurface alloys. Chem 6, 31003117 (2020).

Article Google Scholar

Esterhuizen, J. A., Goldsmith, B. R. & Linic, S. Interpretable machine learning for knowledge generation in heterogeneous catalysis. Nat. Catal. 5, 175184 (2022).

Article Google Scholar

Andersen, M. & Reuter, K. Adsorption enthalpies for catalysis modeling through machine-learned descriptors. Acc. Chem. Res. 54, 27412749 (2021).

Article Google Scholar

Gu, G. H., Lee, M., Jung, Y. & Vlachos, D. G. Automated exploitation of the big configuration space of large adsorbates on transition metals reveals chemistry feasibility. Nat. Commun. 13, 2087 (2022).

Article Google Scholar

Gu, G. H. et al. Practical deep-learning representation for fast heterogeneous catalyst screening. J. Phys. Chem. Lett. 11, 31853191 (2020).

Article Google Scholar

Back, S. et al. Convolutional neural network of atomic surface structures to predict binding energies for high-throughput screening of catalysts. J. Phys. Chem. Lett. 10, 44014408 (2019).

Article Google Scholar

Omidvar, N. et al. Interpretable machine learning of chemical bonding at solid surfaces. J. Phys. Chem. Lett. 12, 1147611487 (2021).

Article Google Scholar

Sanchez-Lengeling, B., Reif, E., Pearce, A & Wiltschko, A. B. A gentle introduction to graph neural networks. Distill https://doi.org/10.23915/distill.00033 (2021).

Mercado, R. et al. Graph networks for molecular design. Mach. Learn. Sci. Technol. 2, 025023 (2021).

Article Google Scholar

Zhou, J. et al. Graph neural networks: a review of methods and applications. AI Open 1, 5781 (2020).

Article Google Scholar

Duvenaud, D. K. et al. Convolutional networks on graphs for learning molecular fingerprints. In Advances in Neural Information Processing Systems Vol. 28 (eds Cortes, C. et al.) (Curran Associates, 2015).

Reiser, P. et al. Graph neural networks for materials science and chemistry. Commun. Mater. 3, 93 (2022).

Article Google Scholar

Schtt, K. T., Sauceda, H. E., Kindermans, P. J., Tkatchenko, A. & Mller, K. R. SchNeta deep learning architecture for molecules and materials. J. Chem. Phys. 148, 241722 (2018).

Article Google Scholar

Gilmer, J., Schoenholz. S. S., Riley, P. F., Vinyals, O. & Dahl, G. E. Neural message passing for quantum chemistry. In Proc. 34th International Conference on Machine Learning: Proc. Machine Learning Research Vol. 70 (eds Precup, D. & Teh, Y. W.) 12631272 (PMLR, 2017).

Zhang, D., Xia, S. & Zhang, Y. Accurate prediction of aqueous free solvation energies using 3D atomic feature-based graph neural network with transfer learning. J. Chem. Inf. Model. 62, 18401848 (2022).

Article Google Scholar

Schtt, K., Unke, O. & Gastegger, M. Equivariant message passing for the prediction of tensorial properties and molecular spectra. In Proc. 38th International Conference on Machine Learning: Proc. Machine Learning Research Vol. 139 (eds Meila, M. & Zhang, T.) 93779388 (PMLR, 2021).

Xie, T. & Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120, 145301 (2018).

Article Google Scholar

Chen, C., Ye, W., Zuo, Y., Zheng, C. & Ong, S. P. Graph networks as a universal machine learning framework for molecules and crystals. Chem. Mater. 31, 35643572 (2019).

Article Google Scholar

Chanussot, L. et al. Open Catalyst 2020 (OC20) dataset and community challenges. ACS Catal. 11, 60596072 (2021).

Article Google Scholar

Tran, R. et al. The Open Catalyst 2022 (OC22) dataset and challenges for oxide electrocatalysis. ACS Catal. 13, 30663084 (2023).

Article Google Scholar

Gasteiger, J., Gross, J. & Gnnemann, S. Directional message passing for molecular graphs. Preprint at https://arxiv.org/abs/2003.03123 (2020).

Kolluru, A. et al. Transfer learning using attentions across atomic systems with graph neural networks (TAAG). J. Chem. Phys. 156, 184702 (2022).

Article Google Scholar

Ghanekar, P. G., Deshpande, S. & Greeley, J. Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis. Nat. Commun. 13, 5788 (2022).

Article Google Scholar

Xu, W., Reuter, K. & Andersen, M. Predicting binding motifs of complex adsorbates using machine learning with a physics-inspired graph representation. Nat. Comput. Sci. 2, 443450 (2022).

Article Google Scholar

Gu, G. H., Plechac, P. & Vlachos, D. G. Thermochemistry of gas-phase and surface species via LASSO-assisted subgraph selection. React. Chem. Eng. 3, 454466 (2018).

Article Google Scholar

Gasteiger, J., Becker, F. & Gnnemann, S. GemNet: universal directional graph neural networks for molecules. In Advances in Neural Information Processing Systems Vol. 34 (eds Ranzato, M.) 67906802 (Curran Associates, 2021).

Sanchez-Lengeling, B. et al. Machine learning for scent: learning generalizable perceptual representations of small molecules. Preprint at https://arxiv.org/abs/1910.10685 (2019).

Flam-Shepherd, D., Wu, T. C., Friederich, P. & Aspuru-Guzik, A. Neural message passing on high order paths. Mach. Learn. Sci. Technol. 2, 045009 (2021).

Article Google Scholar

Morandi, S., Pablo-Garca, S. & Ivkovi, . Title. FG-dataset. ioChem-BD https://doi.org/10.19061/iochem-bd-1-257 (2023).

lvarez-Moreno, M. et al. Managing the computational chemistry big data problem: the ioChem-BD platform. J. Chem. Inf. Model. 55, 95103 (2014).

Article Google Scholar

Isayev, O. et al. Universal fragment descriptors for predicting properties of inorganic crystals. Nat. Commun. 8, 15679 (2017).

Article Google Scholar

Cordero, B. et al. Covalent radii revisited. Dalton Trans. 21, 28322838 (2008).

Article Google Scholar

Ramakrishnan, R., Dral, P. O., Rupp, M. & von Lilienfeld, O. A. Big data meets quantum chemistry approximations: the -machine learning approach. J. Chem. Theory Comput. 11, 20872096 (2015).

Article Google Scholar

Hamilton, W., Ying, Z. & Leskovec, J. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems Vol. 30 (eds Guyon, I. et al.) (Curran Associates, 2017).

Baek, J., Kang, M. & Hwang, S. J. Accurate learning of graph representations with graph multiset pooling. Preprint at https://arxiv.org/abs/2102.11533 (2021).

Wellendorff, J. et al. A benchmark database for adsorption bond energies to transition metal surfaces and comparison to selected DFT functionals. Surf. Sci. 640, 3644 (2015).

Article Google Scholar

Woller, T. et al. Performance of electronic structure methods for the description of HckelMbius interconversions in extended -systems. J. Phys. Chem. A 124, 23802397 (2020).

Article Google Scholar

Sylvetsky, N., Banerjee, A., Alonso, M. & Martin, J. M. L. Performance of localized coupled cluster methods in a moderately strong correlation regime: HckelMbius interconversions in expanded porphyrins. J. Chem. Theory Comput. 16, 36413653 (2020).

Article Google Scholar

Calle-Vallejo, F., Loffreda, D., Koper, M. T. M. & Sautet, P. Introducing structural sensitivity into adsorption-energy scaling relations by means of coordination numbers. Nat. Chem. 7, 403410 (2015).

Article Google Scholar

Li, Q. & Lpez, N. Chirality, rigidity, and conjugation: a first-principles study of the key molecular aspects of lignin depolymerization on Ni-based catalysts. ACS Catal. 8, 42304240 (2018).

Article Google Scholar

Visit link:

Fast evaluation of the adsorption energy of organic molecules on ... - Nature.com

Related Posts