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26 results on '"Xie, Tianjun"'

23. Insights into how the aqueous environment influences the kinetics and mechanisms of heterogeneously-catalyzed COH* and CH3OH* dehydrogenation reactions on Pt(111).

Author

Bodenschatz, Cameron J., Xie, Tianjun, Zhang, Xiaohong, and Getman, Rachel B.

Abstract

Water influences catalytic reactions in multiple ways, including energetic and mechanistic effects. While simulations have provided significant insight into the roles that H2O molecules play in aqueous-phase heterogeneous catalysis, questions still remain as to the extent to which H2O structures influence catalytic mechanisms. Specifically, influences of the configurational variability in the water structures at the catalyst interface are yet to be understood. Configurational variability is challenging to capture, as it requires multiscale approaches. Herein, we apply a multiscale sampling approach to calculate reaction thermodynamics and kinetics for COH* dehydrogenation to CO* and CH3OH* dehydrogenation to CH2OH* on Pt(111) catalysts under liquid H2O. We explore various pathways for these dehydrogenation reactions that could influence the overall mechanism of methanol decomposition by including participation of H2O structures both energetically and mechanistically. We find that the liquid H2O environment significantly influences the mechanism of COH* dehydrogenation to CO* but leaves the mechanism of CH3OH* dehydrogenation to CH2OH* largely unaltered. [ABSTRACT FROM AUTHOR]

Published
2019
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24. Machine-Learning-Enabled Thermochemistry Estimator

Author

Xie, Tianjun, Wittreich, Gerhard R., Curnan, Matthew T., Gu, Geun Ho, Seals, Kayla N., and Tolbert, Justin S.

Abstract

Modeling adsorbates on single-crystal metals is critical in rational catalyst design and other research that requires detailed thermochemistry. First-principles simulations via density functional theory (DFT) are among the prevalent tools to acquire such information about surface species. While they are highly dependable, DFT calculations often require intensive computational resources and runtime. These limiting factors become particularly pronounced when investigating large sets of complex molecules on heavy noble metals. Consequently, our ability to explore these species and their corresponding energetics is limited. In this work, we establish a novel framework that utilizes techniques including molecular encoding, descriptor synthesis, and machine learning to overcome the limitation of costly DFT simulations. Simultaneously, we estimate thermochemical information efficiently at the DFT accuracy level. More specifically, we translated our training molecules into text-based identifiers through a simplified molecular-input line-entry system. Following that, we parametrize our training matrices with sets of short-range descriptors based on group methods, applying first the nearest neighbors to account for linear contributions. This is coupled with the long-range descriptors characterizing second nearest neighbors to account for nonlinear corrections. Finally, we use linear regression and machine learning techniques, such as Gaussian process regressions to regress over the linear and nonlinear matrix systems, respectively. This is the first work to our knowledge that encompasses both the first and second nearest neighbors based on the group theory throughout the featurization, training, and deployment stages. We trained and validated our models with 459 surface species on Pt(111), Ru(0001), and Ir(111) surfaces. Results exhibit robust performance to reproduce the energetics of interest, such as enthalpies, entropies, and heat capacities, at various temperatures. Notably, the mean absolute errors can be reduced by 48% during training and 19% during prediction at a minimum, when compared to the classical group method. Leveraging the novel framework, our machine-learning-enabled thermochemistry estimator significantly empowers us to research the thermochemistry of complex species on metal catalysts.

Published
2024
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26. Insights into how the aqueous environment influences the kinetics and mechanisms of heterogeneously-catalyzed COH* and CH 3 OH* dehydrogenation reactions on Pt(111).

Author

Bodenschatz CJ, Xie T, Zhang X, and Getman RB

Abstract

Water influences catalytic reactions in multiple ways, including energetic and mechanistic effects. While simulations have provided significant insight into the roles that H2O molecules play in aqueous-phase heterogeneous catalysis, questions still remain as to the extent to which H2O structures influence catalytic mechanisms. Specifically, influences of the configurational variability in the water structures at the catalyst interface are yet to be understood. Configurational variability is challenging to capture, as it requires multiscale approaches. Herein, we apply a multiscale sampling approach to calculate reaction thermodynamics and kinetics for COH* dehydrogenation to CO* and CH3OH* dehydrogenation to CH2OH* on Pt(111) catalysts under liquid H2O. We explore various pathways for these dehydrogenation reactions that could influence the overall mechanism of methanol decomposition by including participation of H2O structures both energetically and mechanistically. We find that the liquid H2O environment significantly influences the mechanism of COH* dehydrogenation to CO* but leaves the mechanism of CH3OH* dehydrogenation to CH2OH* largely unaltered.

Published
2019
Full Text
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