The use of machine learning (ML) to guide materials search can lead to order of magnitude acceleration in finding new best-in class materials. Many steps are necessary for such ML-guided approaches to be successful and trustworthy. In this talk we focus on two specific…
High-fidelity computational simulations and physical experiments of hypersonic flows are resource intensive. Training scientific machine learning (SciML) models on limited high-fidelity data offers one approach to rapidly predict behaviors for situations that have not been seen before. However, high-fidelity data is itself in limited…
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…