1 code implementation • 1 Sep 2022 • Yan Xiang, Yu-Hang Tang, Zheng Gong, Hongyi Liu, Liang Wu, Guang Lin, Huai Sun
We introduce an explorative active learning (AL) algorithm based on Gaussian process regression and marginalized graph kernel (GPR-MGK) to explore chemical space with minimum cost.
1 code implementation • 24 Feb 2022 • Yuanran Zhu, Yu-Hang Tang, Changho Kim
We devise mechanisms for training the neural network model to reproduce the correct \emph{statistical} behavior of a target stochastic process.
no code implementations • 15 Jun 2021 • Peiyuan Gao, Xiu Yang, Yu-Hang Tang, Muqing Zheng, Amity Anderson, Vijayakumar Murugesan, Aaron Hollas, Wei Wang
The solvation free energy of organic molecules is a critical parameter in determining emergent properties such as solubility, liquid-phase equilibrium constants, and pKa and redox potentials in an organic redox flow battery.
no code implementations • 3 Jun 2021 • Elizaveta Rebrova, Yu-Hang Tang
We introduce and investigate matrix approximation by decomposition into a sum of radial basis function (RBF) components.
no code implementations • 21 Mar 2021 • Yu-Hang Tang, Yuanran Zhu, Wibe A. de Jong
Optimizing the noise model using maximum likelihood estimation leads to the containment of the GPR model's predictive error by the posterior standard deviation in leave-one-out cross-validation.
no code implementations • 14 Oct 2019 • Yu-Hang Tang, Oguz Selvitopi, Doru Popovici, Aydın Buluç
To cope with the gap between the instruction throughput and the memory bandwidth of current generation GPUs, our solver forms the tensor product linear system on-the-fly without storing it in memory when performing matrix-vector dot product operations in PCG.
2 code implementations • 25 Mar 2019 • Yidong Xia, Ansel Blumers, Zhen Li, Lixiang Luo, Yu-Hang Tang, Joshua Kane, Hai Huang, Matthew Andrew, Milind Deo, Jan Goral
Lastly, we demonstrate, through a flow simulation in realistic shale pores, that the CPU counterpart requires 840 Power9 cores to rival the performance delivered by our package with four V100 GPUs on ORNL's Summit architecture.
Computational Physics
no code implementations • 16 Oct 2018 • Yu-Hang Tang, Wibe A. de Jong
Data-driven prediction of molecular properties presents unique challenges to the design of machine learning methods concerning data structure/dimensionality, symmetry adaption, and confidence management.
2 code implementations • 18 Nov 2016 • Ansel L. Blumers, Yu-Hang Tang, Zhen Li, Xuejin Li, George E. Karniadakis
We observe a speedup of 10. 1 on one GPU over all 16 cores within a single node, and a weak scaling efficiency of 91% across 256 nodes.
Computational Physics Biological Physics