在贝叶斯优化中对分子和材料的自适应表示.
Mahyar Rajabi-Kochi1, Negareh Mahboubi2, Aseem Partap Singh Gill1
1Chemical Engineering & Applied Chemistry, University of Toronto Toronto Ontario M5S 3E5 Canada mohamad.moosavi@utoronto.ca.
Chemical science
|February 27, 2025
概括
特性 适应贝叶斯优化 (FABO) 在优化过程中动态调整分子表示,优于固定方法和随机搜索材料发现. 这种方法在复杂的搜索空间中增强了自动发现.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 人工智能的人工智能
背景情况:
- 贝叶斯优化 (BO) 对自动化材料发现至关重要,但严重依赖于有效的分子表示.
- 专家或数据驱动方法选择的固定表示可能是不理想的或有偏见的,特别是对于具有有限数据的新任务.
- 特征向量的完整性和紧性显著影响BO效率.
研究的目的:
- 引入一个特征适应贝叶斯优化 (FABO) 框架,将动态特征选择集成到BO过程中.
- 为了使BO能够在整个优化周期中调整材料表示,提高效率和准确性.
- 在新型分子优化任务中解决固定表示的局限性.
主要方法:
- 开发了FABO,一个框架,将特征选择与贝叶斯优化循环中的高斯过程集成在一起.
- 在多个优化周期中动态调整的材料表示 (特征向量).
- 评估了FABO的分子优化任务,包括发现金属有机框架 (MOF).
主要成果:
- 与使用预定义特征空间的随机搜索和方法相比,FABO表现出更高的性能.
- 适应性表示方法成功地确定了不同MOF发现任务的有效特征,具有不同的属性分布.
- 对于已知的任务,FABO与人类的化学直觉保持一致,并证明对新的优化挑战具有强大作用.
结论:
- 在自动化发现中,FABO提供了一种强大的方法来导航复杂的材料搜索空间.
- 动态特征适应对于优化贝叶斯优化性能至关重要,特别是当先前知识有限时.
- 该框架提高了自动化材料发现活动的效率和可靠性.
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