2025-04-09 09:00:00
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…
Rapid characterization of microstructural properties such as phase, crystallographic texture, and grain size distribution is critical to accelerating the discovery and development of new materials and processing routes. Our goal in this work is to increase the throughput of microstructure characterization by transmission high-energy…
The discovery of new structural materials is slow, costly and limited, particularly for applications in extreme conditions that are challenging to mimic for characterization of properties. To address this challenge with a focus on high temperature applications, we are developing a framework to discover…
We study the fragmentation response of a thin ring undergoing radially expanding loading, as the analysis can be carried out in 1D and all the domain undergoes a spatially uniform and temporally increasing loading. We assume that the underlying material properties are random fields….
During Phase I of the HTMDEC program, the BIRDSHOT team focused on demonstrating the capability to execute each of the HTMDEC major capability thrusts (high-throughput synthesis, characterization, testing, simulation) in an integrated manner within a principled iterative Bayesian Optimization (BO) framework. We aimed to…
Current material genomics approaches face challenges in efficiently exploring the high dimensional design space of polymer-bonded explosives (PBX), which play a critical role in understanding their sensitivity under shock loading conditions. These challenges lead to high computational costs and limited insights. Our innovative Bayesian…
Over 50 years into their pivotal role in scientific exploration, materials and molecular modeling encounter challenges stemming from diverse code structures, languages, and input styles. The complexity escalates with the rise of machine learning. Addressing this, the open-source community explores solutions, from scripting libraries…
