可扩展的加速材料 通过自主实验和协作学习来发现可持续的基于多糖的基
Yang Liu1,2, Xubo Yue3, Junru Zhang1
1Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States.
ACS applied materials & interfaces
|December 11, 2024
概括
使用共识贝叶斯优化协作学习加速材料发现. 这种方法增强了自主实验 (AE),使多个利益相关者能够有效地发现优化的复合多糖化物水凝.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 计算科学 计算科学
背景情况:
- 独立的自动驾驶工作流可以发现和设计材料.
- 协调多个利益相关者和工作流程是推动加速材料发现 (AMD) 自主实验 (AE) 的关键.
研究的目的:
- 描述基于AE和"协作学习"的可扩展的AMD范式.
主要方法:
- 为协作学习开发了一种新的共识贝叶斯优化 (BO) 模型.
- 协作工作流与非协作AMD工作流进行了比较.
主要成果:
- 同意的BO模型使得快速发现机械优化的复合多糖化合物水凝成为可能.
- 在八次实验代中,协作工作流表现优于非协作工作流.
- 四个合作客户在五次代后实现了显著的材料性能.
结论:
- 通过共识的协作学习 (BO) 可以实现自动驾驶材料研究的扩展和性能优化.
- 这种方法促进了人类和机器之间的合作,以实现共享的材料设计目标.
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