Mapping pareto fronts for efficient multi-objective materials discovery

• Main Authors: Low, Andre Kai Yuan, Vissol-Gaudin, Eleonore, Lim, Yee-Fun, Hippalgaonkar, Kedar
• 其他作者: School of Materials Science and Engineering
• 格式: Article
• 語言: English
• 出版: 2024
• 主題: Engineering; Machine learning; High-throughput experimentation
• 在線閱讀: https://hdl.handle.net/10356/175894
• 機構: Nanyang Technological University

With advancements in automation and high-throughput techniques, we can tackle more complex multi-objective materials discovery problems requiring a higher evaluation budget. Given that experimentation is greatly limited by evaluation budget, maximizing sample efficiency of optimization becomes crucial. We discuss the limitations of using hypervolume as a performance indicator and propose new metrics relevant to materials experimentation: such as the ability to perform well for complex high-dimensional problems, minimizing wastage of evaluations, consistency/robustness of optimization, and ability to scale well to high throughputs. With these metrics, we perform an empirical study of two conceptually different and state-of-the-art algorithms (Bayesian and Evolutionary) on synthetic and real-world datasets. We discuss the merits of both approaches with respect to exploration and exploitation, where fully resolving the Pareto Front provides more knowledge of the best material.