5 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…
Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost
Ilgar Baghishov, Jan Janssen, Graeme Henkelman +1
Machine-learned interatomic potentials (MLIPs) are revolutionizing computational materials science and chemistry by offering an efficient alternative to {\em ab initio} molecular d…
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…
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…
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…