7 citations · 12 across the 7 of their papers we have counts for
7 papers
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…
DataScribe: An AI-Native, Policy-Aligned Web Platform for Multi-Objective Materials Design and Discovery
Divyanshu Singh, Doguhan Sarıtürk, Cameron Lea +3
The acceleration of materials discovery requires digital platforms that go beyond data repositories to embed learning, optimization, and decision-making directly into research work…
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…
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…
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…