13 citations · 22 across the 10 of their papers we have counts for
5 papers · 1 filter
Deep Gaussian Process-based Cost-Aware Batch Bayesian Optimization for Complex Materials Design Campaigns
Sk Md Ahnaf Akif Alvi, Brent Vela, Vahid Attari +4
The accelerating pace and expanding scope of materials discovery demand optimization frameworks that efficiently navigate vast, nonlinear design spaces while judiciously allocating…
Data Driven Insights into Composition Property Relationships in FCC High Entropy Alloys
Nicolas Flores, Daniel Salas Mula, Wenle Xu +15
Structural High Entropy Alloys (HEAs) are crucial in advancing technology across various sectors, including aerospace, automotive, and defense industries. However, the scarcity of…
Accurate and Uncertainty-Aware Multi-Task Prediction of HEA Properties Using Prior-Guided Deep Gaussian Processes
Sk Md Ahnaf Akif Alvi, Mrinalini Mulukutla, Nicolas Flores +6
Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys(HEAs), especially when integrating computational predi…
Mapping of Microstructure Transitions during Rapid Alloy Solidification Using Bayesian-Guided Phase-Field Simulations
José Mancias, Brent Vela, Juan Flórez-Coronel +4
This study addresses microstructure selection mechanisms in rapid solidification, specifically targeting the transition from cellular/dendritic to planar interface morphologies und…
Towards Autonomous Experimentation: Bayesian Optimization over Problem Formulation Space for Accelerated Alloy Development
Danial Khatamsaz, Joseph Wagner, Brent Vela +2
Accelerated discovery in materials science demands autonomous systems capable of dynamically formulating and solving design problems. In this work, we introduce a novel framework t…