Sam Reeve Computational Scientist Contact 865.576.5617 | REEVEST@ORNL.GOV All Publications ExaCA v2.0: A versatile, scalable, and performance portable cellular automata application for additive manufacturing solidification ExaCA: A performance portable exascale cellular automata application for alloy solidification modeling Cabana: A Performance Portable Library for Particle-Based Simulations Implementing a neural network interatomic model with performance portability for emerging exascale architectures Myna: Connecting Powder Bed Fusion Build Data to Simulation Tools for Digital Twin Applications On the numerical sensitivity of cellular automata grain structure predictions to large thermal gradients and cooling rates Toucan: A Performance Portable, Scalable Implementation of the DECA Algorithm... Co-design for Particle Applications at Exascale PDMATLAB2D: A Peridynamics MATLAB Two-dimensional Code A Sparse Distributed Gigascale Resolution Material Point Method... Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems Fast and Accurate Predictions of Total Energy for Solid Solution Alloys with Graph Convolutional Neural Networks ExaAM: Metal additive manufacturing simulation at the fidelity of the microstructure... Enabling particle applications for exascale computing platforms Adaptive Spatially Aware I/O for Multiresolution Particle Data Layouts Multiferroic ground states in free standing perovskite-based nanodots: a density functional theory study Key Links Google Scholar GitHub Organizations Computing and Computational Sciences Directorate Computational Sciences and Engineering Division Advanced Computing Methods for Engineered Systems Section Scalable Engineering Applications Group
Research Highlight ExaCA: a performance portable exascale cellular automata application for alloy solidification modeling
Research Highlight Deep Learning for Fast and Accurate Predictions of Crystallographic and Thermodynamic Properties of Multi-Component Solid Solution Alloys
Research Highlight HydraGNN - Distributed PyTorch Implementation of Multi-Headed Graph Convolutional Neural Networks
