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arXiv 2203.17049cond-mat.mtrl-sci
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用自主扫描液滴池实现耐蚀合金涂层的自动化设计

Towards automated design of corrosion resistant alloy coatings with an autonomous scanning droplet cell

Brian DeCost, Howie Joress, Suchismita Sarker, Apurva Mehta, Jason Hattrick-Simpers

摘要:

我们提出了一种自主扫描液滴池平台,设计用于按需合金电沉积和实时电化学表征,用于研究多组分合金的耐腐蚀性能。自动化和机器学习目前正在推动高通量和自主材料设计和发现的快速创新。我们提出了两个合金设计案例研究:一个侧重于多目标耐腐蚀合金优化,一个案例研究强调了多峰表征的复杂性,这些表征需要深入了解驱动所观察到的材料行为的潜在结构和化学因素。这激发了自主研究平台与科学机器学习方法之间的紧密耦合,该方法融合了机械物理模型和黑箱机器学习模型。这一新兴的研究领域提供了新的机会,以加速材料的合成,评估,从而发现和设计。

英文摘要:

We present an autonomous scanning droplet cell platform designed for on-demand alloy electrodeposition and real-time electrochemical characterization for investigating the corrosion-resistance properties of multicomponent alloys. Automation and machine learning are currently driving rapid innovation in high throughput and autonomous materials design and discovery. We present two alloy design case studies: one focusing on a multi-objective corrosion resistant alloy optimization, and a case study highlighting the complexity of the multimodal characterization needed to provide insight into the underlying structural and chemical factors that drive observed material behavior. This motivates a close coupling between autonomous research platforms and scientific machine learning methodology that blends mechanistic physical models and black box machine learning models. This emerging research area presents new opportunities to accelerate materials synthesis, evaluation, and hence discovery and design.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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