The underlying mechanisms associated with dynamical failure are not fully understood, this is particularly true regarding the plastic deformation around the nucleation and growth of porosity that eventually leads to failure. We used large-scale non-equilibrium molecular dynamics simulations to investigate the shock loading and…
Understanding the damage and failure of structural materials under extreme mechanical loads such as hypervelocity impact, which combines high strain rates, tri-axial stress, and temperature, is important for a range of applications including space exploration and defense. Niobium (Nb) is a BCC transition metal…
Refractory alloys are promising for high-temperature applications because of their high strength at elevated temperatures, high thermal conductivity, and low thermal expansion coefficients. One hundred twenty-five refractory high entropy alloys (RHEAs) have been designed to exhibit a single BCC phase at elevated temperatures, target…
Stress field prediction is typically provided by means of Finite Element Analysis (FEA) which can become computationally prohibitive considering complex material behavior. Such a limitation is especially important within the context of structure–property exploration and inverse design for materials discovery where a larger number…
Designing materials using a combinatorial approach to achieve desired mechanical properties requires multiple iterations. The cost of conducting experiments for each iteration to determine the material properties can be prohibitively high. One alternative is to use computational models, but running high-fidelity physics-based simulations can…
A bottleneck in high-throughput nanomaterials discovery is the pace at which new materials can be structurally characterized. Although current machine learning (ML) methods show promise for the automated processing of electron diffraction patterns (DPs), they fail in high-throughput experiments where DPs are collected from…
Designing new metallic materials is costly and time-consuming, due to the inefficiencies in computational predictions, sample fabrication and extensive microstructural characterization required. Even with machine learning aided design, compositional choices require bulk samples to be produced and validate the prediction microstructural data. To facilitate…
Recent advances in scientific programming, particularly with regards to topology optimization and machine learning, have necessitated computational methods that generate gradients for optimization. One method to do so is to utilize a tool called automatic differentiation, a mechanism to algorithmically calculate derivatives of functions…
Developing high-fidelity accurate machine learning (ML) models for alloy design requires large data sets encompassing the gamut of physical material properties. Typically, properties highly correlated with desirable material behavior (e.g., hardness, toughness) receive the most attention. Due to the quantity of data required for…
The face centered cubic (FCC) complex concentrated alloys (CCA) compositional space is expansive, making it almost impossible to discover the most promising alloy compositions using conventional approaches. As part of the High Throughput Materials Discovery for Extreme Conditions (HTMDEC) initiative, the compositional space of…