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Published in Nature communications (03.04.2018)
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Deep neural network learning of complex binary sorption equilibria from molecular simulation dataElectronic supplementary information (ESI) available: Experimental details for machine learning and Monte Carlo simulation for binary sorption systems investigated, neural network prediction results for equilibrium loadings in systems other than MFI-C5-W, and additional method details for desorption operation optimization. See DOI: 10.1039/c8sc05340e
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Deep neural network learning of complex binary sorption equilibria from molecular simulation data† †Electronic supplementary information (ESI) available: Experimental details for machine learning and Monte Carlo simulation for binary sorption systems investigated, neural network prediction results for equilibrium loadings in systems other than MFI-C5-W, and additional method details for desorption operation optimization. See DOI: 10.1039/c8sc05340e
Sun, Yangzesheng, DeJaco, Robert F., Siepmann, J. Ilja
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Published in Chemical science (Cambridge) (18.03.2019)
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Deep neural network learning of complex binary sorption equilibria from molecular simulation data
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Published in Chemical science (Cambridge) (01.01.2019)
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Published in Fluid phase equilibria (15.11.2020)
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Multiple linear regression and thermodynamic fluctuations are equivalent for computing thermodynamic derivatives from molecular simulation
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Published in Fluid phase equilibria (01.11.2020)
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Published in Fluid phase equilibria (01.11.2020)
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Assessing the Quality of Molecular Simulations for Vapor–Liquid Equilibria: An Analysis of the TraPPE Database
Eggimann, Becky L., Sun, Yangzesheng, DeJaco, Robert F., Singh, Ramanish, Ahsan, Muhammad, Josephson, Tyler R., Siepmann, J. Ilja
Published in Journal of chemical and engineering data (03.10.2019)
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Published in Journal of chemical and engineering data (03.10.2019)
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Fingerprinting diverse nanoporous materials for optimal hydrogen storage conditions using meta-learning
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Published in Science advances (01.07.2021)
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