2025-04-09 09:00:00
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…
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…
