Engineering topics
Wood, Mitchell
Publications and source records attributed to Wood, Mitchell.
Multiscale development of predictive constitutive models to assess critical hotspots and microstructure sensitivity.
Abstract not provided.
FitSNAP : Scalable Solutions for Training Machine Learned Interatomic Potentials.
Abstract not provided.
Scalable Solutions for Training Machine Learned Interatomic Potentials.
Abstract not provided.
Interatomic Potentials for Materials Science and Beyond; Advances in Machine Learned Spectral Neighborhood Analysis Potentials.
Abstract not provided.
Molecular Dynamics Simulations of Hydrogen and Nitrogen Implantation in Tungsten.
Abstract not provided.
Data Mining the Mesoscale to Study Shock Ignition and Reaction Growth in Pressed Energetic Materials .
Abstract not provided.
Development of predictive multiscale constitutive models for pressed energetic materials to resolve the shock to detonation transition.
Abstract not provided.
Development of SNAP Interatomic Potentials for Gas-Metal Interactions for Fusion Energy Materials.
Abstract not provided.
Data-driven Magnetic Materials Modeling; Advances in Classical Molecular Dynamics.
Abstract not provided.
Scalable Solutions for Training Machine Learned Interatomic Potentials.
Abstract not provided.
Training Atomic Cluster Expansion Potentials using LAMMPS and FitSNAP.
Abstract not provided.
Training Machine Learned Interatomic Potentials for Chemical Complexity - Application to Refractory CCAs .
Abstract not provided.
Probing Structural and Magnetic Phase Changes in the Shock Response of Iron with Molecular Dynamics.
Abstract not provided.
Expediting the materials discovery process of MPEAs through atomistic modeling and machine learning techniques.
Abstract not provided.
Development of SNASP Machine Learned Interatomic Potentials for Materials in Extreme Environments.
Abstract not provided.
Sandia / IBM Discussion on Machine Learning for Materials Applications [Slides]
This report includes a compilation of several slide presentations: 1) Interatomic Potentials for Materials Science and Beyond–Advances in Machine Learned Spectral Neighborhood Analysis Potentials (Wood); 2) Agile Materials Science and Advanced Manufacturing through AI/ML (de Oca Zapiain); 3) Machine Learning for DFT Calculations (Rajamanickam); 4) Structure-preserving ML discovery of a quantum-to-continuum codesign stack (Trask); and 5) IBM Overview of Accelerated Discovery Technology (Pitera)