7 citations · 12 across the 7 of their papers we have counts for
5 papers · 1 filter
Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design
Sk Md Ahnaf Akif Alvi, Jan Janssen, Danny Perez +2
Closed-loop materials discovery iterates between proposing candidate structures and evaluating their properties, and property evaluation dominates the cost. In the generative varia…
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…
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…
Decoding Non-Linearity and Complexity: Deep Tabular Learning Approaches for Materials Science
Vahid Attari, Raymundo Arroyave
Materials data, especially those related to high-temperature properties, pose significant challenges for machine learning models due to extreme skewness, wide feature ranges, modal…