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Found 4 papers, 2 papers with code
Data Distillation for Neural Network Potentials toward Foundational Dataset
Furthermore, the data can be translated to other metallic systems (aluminum and niobium), without repeating the sampling and distillation processes.
Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules
Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures.
Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems
We train HydraGNN on an open-source ab initio density functional theory (DFT) dataset for iron-platinum (FePt) with a fixed body centered tetragonal (BCT) lattice structure and fixed volume to simultaneously predict the mixing enthalpy (a global feature of the system), the atomic charge transfer, and the atomic magnetic moment across configurations that span the entire compositional range.
On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective
We implement and benchmark parallel I/O methods for the fully-manycore driven particle-in-cell code PIConGPU.
Performance Computational Physics D.4.8; B.4.3; I.6.6
