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We present a machine learning framework, leveraging several tools from Materials Informatics and microstructure-explicit simulations, to rapidly explore these high-dimensional spaces and establish structure-property relationships for material systems. The framework allows for a rapid establishment of a learned structure-property relationship for enabling materials design…
The development of Machine Learning Interatomic Potentials (MLIPs) has gained significant traction in recent years. These new data-driven potential energy approximations often lack the physics-based foundations that inform many traditionally-developed interatomic potentials and hence require robust validation methods for their applicability, accuracy, computational efficiency,…
Multi-principal metal alloys (MPEAs) are an active research area for their desirable mechanical performance and electrochemical resistance. However, design of new MPEAs is complex. The potential composition space for MPEAs is massive and Edisonian approaches are slow. Artificial intelligence (AI) guided discovery is promising…
Machine learning approaches to materials discovery have great potential, but currently face some limitations in data availability, curation, and potential biases. One promising approach is using generative machine learning models to produce new data points representing novel material compositions and structures. This can help…
Microstructural heterogeneity affects the macroscale behavior of materials. Therefore, optimizing macroscale material performance requires designing material at the micro-scale. However, conventional numerical approaches face significant challenges in the practical application of multiscale material design, optimization, and uncertainty quantification. To overcome this, we developed an…
Refractory multi-principal element alloys (RMPEAs) exhibit favorable characteristics for numerous high-temperature applications. However, there is a lack of accurate approaches for tailoring the composition of RMPEAs to achieve multiple desired properties. The primary challenge results from the extensive and intricate design space that needs…
The two-stage light gas gun has been the predominant tool in hypervelocity-related research. Due to the high compression rate of light gases, such as hydrogen and helium, the compressed gas reaches extreme pressure and temperature levels, measured in thousands of bars and Kelvins. This…
We present a combined theoretical/computational framework to model jetting processes following shockwave/interface interactions. The model is based off treating the vorticity field as the principal independent variable. Following classical vortex dynamics, we develop a low dimensional set of ODEs which are easily integrated to…
The Office of Science and Technology Policy created the Orbital Debris Interagency Working Group (ODIWG), including members of the DoD, NASA, USAF, and USSF to tackle the challenge of space debris. Whipple shields used by these groups have significant limitations. An improvement that saves…
The first planetary defense mission, DART, proved that it is possible to change the orbital path of an asteroid via kinetic impact. The effectiveness of the kinetic impact method is quantified in terms of the momentum enhancement factor β, which is known to depend…
