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贝叶斯共导航:通过主动学习对材料数字双胞胎进行动态设计
Boris N Slautin1, Yongtao Liu2, Hiroshi Funakubo3
1Institute for Materials Science and Center for Nanointegration Duisburg-Essen (CENIDE), University of Duisburg-Essen, Essen 45141, Germany.
ACS nano
|August 26, 2024
概括
这项研究引入了一种新的方法,通过将理论模型与自动化实验相结合,加速科学发现. 这种"循环中的理论"方法使用贝叶斯共导航来即时更新模型,减少不确定性并为复杂材料创建数字双胞胎.
科学领域:
- 材料科学 材料科学 材料科学
- 计算科学 计算科学
- 物理化学 物理化学
背景情况:
- 科学进步依赖于理论和实验,但这种循环往往很慢,特别是在复杂的高维系统中.
- 将理论见解直接集成到自动化实验工作流中,对于加速发现至关重要.
研究的目的:
- 开发一种实时将理论集成到自动化实验中的方法,使科学进步更快.
- 通过适应性的理论模型更新,尽量减少实验对象空间中的认识不确定性.
主要方法:
- 贝叶斯对导航方法用于探索理论模型空间和指导实验.
- 同时开发用于模拟和实验领域的替代模型.
- 调整理论模型控制参数以减少不确定性.
主要成果:
- 展示了一种方法来创建物质结构的数字双胞胎,结合代用模型和理论模型.
- 成功地应用了研究铁电材料功能反应的方法.
- 该方法可以在自动化实验设置中实现即时理论更新.
结论:
- 提出的循环理论方法显著加速了科学发现过程.
- 这种方法可以为复杂的材料系统创建精确的数字双胞胎.
- 该方法在各种科学领域具有广泛的适用性,包括分子和纳米集群系统.
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