With the new era of the Army Futures Command, the Army is in the midst of a substantial modernization effort. To meet this challenge at the basic research level, modernized experimentation technologies can be developed that increase the discovery rate of superior performing materials…
Machine learning algorithms, in conjunction with high-throughput atomistic simulations, are used to probe the uncertainty within the energetics, structure, and mechanical response of silicon carbide grain boundaries. In this work, a Metropolis style Monte Carlo algorithm is applied to tilt and twist grain boundaries…
Recent shock physics capability developments at X-ray light sources have resulted in the performance of experiments at much higher rates and with very disparate data types. The data from these experiments commonly consists of velocimetry data that provide information about bulk volumetric response of…
Concurrent in-situ flash X-ray radiography, Photon Doppler Velocimetry (pdv), and high-speed video using the ARL HIDRA provides microsecond-resolved information during ballistic impact experiments. Manual image analysis, although laborious and time intensive, provides characterization of dwell time, penetration velocity, and timing of break-out of the…
The present work focuses on the development of a new class of interatomic potential known as a “physically-informed neural network” (PINN) potential. This new potential format combines the high level of accuracy and flexibility associated with artificial neural networks (ANNs) with the transferability inherent…
This presentation will discuss the use of Adaptive Neural Networks (ANNs) in atomistic computer simulations, focusing on their implementation and efficiency on High-Performance Computing (HPC) massively parallel architectures. This work builds on the emerging effort in materials science to employ machine-learning methods, including ANNs,…
