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Found 9 papers, 4 papers with code
Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular Simulation
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.
Learning Stochastic Dynamics with Statistics-Informed Neural Network
We devise mechanisms for training the neural network model to reproduce the correct \emph{statistical} behavior of a target stochastic process.
Graphical Gaussian Process Regression Model for Aqueous Solvation Free Energy Prediction of Organic Molecules in Redox Flow Battery
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.
Nonlinear Matrix Approximation with Radial Basis Function Components
We introduce and investigate matrix approximation by decomposition into a sum of radial basis function (RBF) components.
Detecting Label Noise via Leave-One-Out Cross-Validation
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.
A High-Throughput Solver for Marginalized Graph Kernels on GPU
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.
A GPU-accelerated package for simulation of flow in nanoporous source rocks with many-body dissipative particle dynamics
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
Prediction of Atomization Energy Using Graph Kernel and Active Learning
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.
GPU-accelerated Red Blood Cells Simulations with Transport Dissipative Particle Dynamics
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
