14 citations · 36 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
Supply Risk-Aware Alloy Discovery and Design
Mrinalini Mulukutla, Robert Robinson, Danial Khatamsaz +3
Materials design is a critical driver of innovation, yet overlooking the technological, economic, and environmental risks inherent in materials and their supply chains can lead to…
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…
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…
Data-driven study of composition-dependent phase compatibility in NiTi shape memory alloys
Sina Hossein Zadeh, Cem Cakirhan, Danial Khatamsaz +5
The martensitic transformation in NiTi-based Shape Memory Alloys (SMAs) provides a basis for shape memory effect and superelasticity, thereby enabling applications requiring solid-…