As computers get faster, researchers—not hardware or algorithms—become the bottleneck in scientific discovery. Computational study of colloidal self‐assembly is one area that is keenly affected: even after computers generate massive amounts of raw data, performing an exhaustive search to determine what (if any) ordered structures occur in a large parameter space of many simulations can be excruciating. We demonstrate how machine learning can be applied to discover interesting areas of parameter space in colloidal self‐assembly. We create numerical fingerprints—inspired by bond orientational order diagrams—of structures found in self‐assembly studies and use these descriptors to both find interesting regions in a phase diagram and identify characteristic local environments in simulations in an automated manner for simple and complex crystal structures. Utilizing these methods allows analysis to keep up with the data generation ability of modern high‐throughput computing environments. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2198–2206, 2018

中文翻译：

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机器学习用于晶体识别和发现

随着计算机变得越来越快，研究人员（而不是硬件或算法）成为科学发现的瓶颈。胶体自组装的计算研究是一个受到严重影响的领域：即使在计算机生成大量原始数据之后，进行详尽的搜索以确定在许多模拟的大参数空间中出现了什么（如果有的话）有序结构，这可能会令人费解。 。我们演示了如何将机器学习应用于胶体自组装中发现有趣的参数空间区域。我们创建自组装研究中发现的结构的数字指纹（受键取向图的启发），并使用这些描述符在相图中找到有趣的区域，并在模拟中自动识别简单和复杂的晶体结构的特征局部环境。利用这些方法，分析可以跟上现代高通量计算环境的数据生成能力。©2018美国化学工程师学会AIChE J，64：2198–2206，2018