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Accelerating material discovery with a threshold-driven hybrid acquisition policy-based Bayesian optimization by Raihan, Ahmed Shoyeb; Khosravi, Hamed; Das, Srinjoy; Ahmed, Imtiaz is a scholarly article available to read on EtoBox.

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Advancements in materials play a crucial role in technological progress. However, the process of discovering and developing materials with desired properties is often impeded by substantial experimental costs, extensive resource utilization, and lengthy development periods. To address these challenges, modern approaches often employ machine learning (ML) techniques such as Bayesian Optimization (BO), which streamline the search for optimal materials by iteratively selecting experiments that are most likely to yield beneficial results. However, traditional BO methods, while beneficial, often struggle with balancing the trade-off between exploration and exploitation, leading to sub-optimal performance in material discovery processes. This paper introduces a novel Threshold-Driven UCB-EI Bayesian Optimization (TDUE-BO) method, which dynamically integrates the strengths of Upper Confidence Bound (UCB) and Expected Improvement (EI) acquisition functions to optimize the material discovery process. Unlike the classical BO, our method focuses on efficiently navigating the high-dimensional material design space (MDS). TDUE-BO begins with an exploration-focused UCB approach, ensuring a compr

Author

Raihan, Ahmed Shoyeb; Khosravi, Hamed; Das, Srinjoy; Ahmed, Imtiaz

Published

2023

Language

EN