6 papers
A BRAVE Alloy Design Campaign (Bayesian Risk-aware Alloy discoVery and Exploration)
Mrinalini Mulukutla, Danial Khatamsaz, Trevor Hastings +23
In constrained alloy optimization, the compositions with the highest performance potential often reside at the boundary of phase stability -- where the risk of experimental failure…
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…
Accelerated Multi-Objective Alloy Discovery through Efficient Bayesian Methods: Application to the FCC Alloy Space
Trevor Hastings, Mrinalini Mulukutla, Danial Khatamsaz +14
This study introduces BIRDSHOT, an integrated Bayesian materials discovery framework designed to efficiently explore complex compositional spaces while optimizing multiple material…
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…
Microstructure-Aware Bayesian Materials Design
Danial Khatamsaz, Vahid Attari, Raymundo Arroyave
In this study, we propose a novel microstructure-sensitive Bayesian optimization (BO) framework designed to enhance the efficiency of materials discovery by explicitly incorporatin…
Hierarchical Gaussian Process-Based Bayesian Optimization for Materials Discovery in High Entropy Alloy Spaces
Sk Md Ahnaf Akif Alvi, Jan Janssen, Danial Khatamsaz +3
Bayesian optimization (BO) is a powerful and data-efficient method for iterative materials discovery and design, particularly valuable when prior knowledge is limited, underlying f…