5 papers · 1 filter
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…
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…
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…
Illustrating an Effective Workflow for Accelerated Materials Discovery
Mrinalini Mulukutla, A. Nicole Person, Sven Voigt +26
Algorithmic materials discovery is a multi-disciplinary domain that integrates insights from specialists in alloy design, synthesis, characterization, experimental methodologies, c…