14 citations · 36 across the 10 of their papers we have counts for
10 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…
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…
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-…
Efficient Propagation of Uncertainty via Reordering Monte Carlo Samples
Danial Khatamsaz, Vahid Attari, Raymundo Arroyave +1
Uncertainty analysis in the outcomes of model predictions is a key element in decision-based material design to establish confidence in the models and evaluate the fidelity of mode…