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Yu-Hang Tang

Showing results (1-10 of 16) with videos related to

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The Journal of Chemical Physics|February 3, 2019
Prediction of atomization energy using graph kernel and active learningYu-Hang Tang, Wibe A de Jong
The Journal of Chemical Physics|January 22, 2018
An atomistic fingerprint algorithm for learning ab initio molecular force fieldsYu-Hang Tang, Dongkun Zhang, George Em Karniadakis
Journal of Chemical Information and Modeling|July 28, 2023
Interpretable Molecular Property Predictions Using Marginalized Graph KernelsYan Xiang, Yu-Hang Tang, Guang Lin, et al.
Journal of Chemical Information and Modeling|November 1, 2021
A Comparative Study of Marginalized Graph Kernel and Message-Passing Neural NetworkYan Xiang, Yu-Hang Tang, Guang Lin, et al.
Physical Review. E|April 15, 2016
Analysis of hydrodynamic fluctuations in heterogeneous adjacent multidomains in shear flowXin Bian, Mingge Deng, Yu-Hang Tang, et al.
Chemical Communications (Cambridge, England)|June 19, 2014
Large-scale dissipative particle dynamics simulations of self-assembled amphiphilic systemsXuejin Li, Yu-Hang Tang, Haojun Liang, et al.
Chemical Communications (Cambridge, England)|June 12, 2015
Mesoscale modeling of phase transition dynamics of thermoresponsive polymersZhen Li, Yu-Hang Tang, Xuejin Li, et al.
The Journal of Physical Chemistry. A|May 17, 2021
Predicting Single-Substance Phase Diagrams: A Kernel Approach on Graph Representations of MoleculesYan Xiang, Yu-Hang Tang, Hongyi Liu, et al.
BMC Genomics|January 29, 2016
Identifying micro-inversions using high-throughput sequencing readsFeifei He, Yang Li, Yu-Hang Tang, et al.
Nature Communications|December 31, 2025
Domain oriented universal machine learning potential enables fast exploration of chemical space of battery electrolytesFeng Wang, Yu-Hang Tang, Ze-Bing Ma, et al.
Pageof 2

Showing results (1-10 of 16) with videos related to

Sort By:
Pageof 2
The Journal of Chemical Physics|February 3, 2019
Prediction of atomization energy using graph kernel and active learningYu-Hang Tang, Wibe A de Jong
The Journal of Chemical Physics|January 22, 2018
An atomistic fingerprint algorithm for learning ab initio molecular force fieldsYu-Hang Tang, Dongkun Zhang, George Em Karniadakis
Journal of Chemical Information and Modeling|July 28, 2023
Interpretable Molecular Property Predictions Using Marginalized Graph KernelsYan Xiang, Yu-Hang Tang, Guang Lin, et al.
Journal of Chemical Information and Modeling|November 1, 2021
A Comparative Study of Marginalized Graph Kernel and Message-Passing Neural NetworkYan Xiang, Yu-Hang Tang, Guang Lin, et al.
Physical Review. E|April 15, 2016
Analysis of hydrodynamic fluctuations in heterogeneous adjacent multidomains in shear flowXin Bian, Mingge Deng, Yu-Hang Tang, et al.
Chemical Communications (Cambridge, England)|June 19, 2014
Large-scale dissipative particle dynamics simulations of self-assembled amphiphilic systemsXuejin Li, Yu-Hang Tang, Haojun Liang, et al.
Chemical Communications (Cambridge, England)|June 12, 2015
Mesoscale modeling of phase transition dynamics of thermoresponsive polymersZhen Li, Yu-Hang Tang, Xuejin Li, et al.
The Journal of Physical Chemistry. A|May 17, 2021
Predicting Single-Substance Phase Diagrams: A Kernel Approach on Graph Representations of MoleculesYan Xiang, Yu-Hang Tang, Hongyi Liu, et al.
BMC Genomics|January 29, 2016
Identifying micro-inversions using high-throughput sequencing readsFeifei He, Yang Li, Yu-Hang Tang, et al.
Nature Communications|December 31, 2025
Domain oriented universal machine learning potential enables fast exploration of chemical space of battery electrolytesFeng Wang, Yu-Hang Tang, Ze-Bing Ma, et al.
Pageof 2