On strong-scaling and open-source tools for analyzing atom probe tomography data
On strong-scaling and open-source tools for analyzing atom probe tomography data
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用于分析原子探针断层扫描数据的强扩展性和开源工具
DOI：
10.1038/s41524-020-00486-1
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发表时间：
2021
影响因子：
9.7
通讯作者：
B. Gault
中科院分区：
文献类型：
--
作者：
M. Kühlbach;P. Bajaj;H. Zhao;M. H. Çelik;E. A. Jägle;B. Gault
通过开放源代码和透明的元数据标准开发用于高通量方法的强伸缩性计算工具，成功地改变了许多计算材料科学界。虽然这样的工具在凝聚态物理学领域已经成熟，但对于许多实验者来说，情况仍然非常不同。原子探针断层摄影术(APT)就是一个例子。这种显微镜和微分析技术已经成熟为一种多功能的纳米分析表征工具，其应用范围从材料科学到地质学，甚至可能超越材料科学。这里，需要数据科学工具来从重建的点云中提取化学结构的空间相关性。对于APT和其他高端分析技术，后处理主要是使用专有软件工具执行的，这些工具的执行不透明，往往性能有限。科学界成员的软件开发改善了这种情况，但与计算材料科学领域的复杂性相比，仍有几个差距。尤其是支持科学计算硬件的开源工具，支持高吞吐量工作流程的工具，以及开放记录良好的元数据标准，以使实验研究更好地与公平的数据管理原则保持一致。为此，我们介绍了一个开源的点云数据科学计算和高通量研究工具--ParProbe，这里以APT为例。我们展示了如何在对多达20亿个离子的后处理APT数据集应用几个计算几何、空间统计和聚类任务的同时量化不确定性。这些工具可以很好地与Python和HDF5配合使用，从而实现几个数量级的性能提升、自动化和重现性。
The development of strong-scaling computational tools for high-throughput methods with an open-source code and transparent metadata standards has successfully transformed many computational materials science communities. While such tools are mature already in the condensed-matter physics community, the situation is still very different for many experimentalists. Atom probe tomography (APT) is one example. This microscopy and microanalysis technique has matured into a versatile nano-analytical characterization tool with applications that range from materials science to geology and possibly beyond. Here, data science tools are required for extracting chemo-structural spatial correlations from the reconstructed point cloud. For APT and other high-end analysis techniques, post-processing is mostly executed with proprietary software tools, which are opaque in their execution and have often limited performance. Software development by members of the scientific community has improved the situation but compared to the sophistication in the field of computational materials science several gaps remain. This is particularly the case for open-source tools that support scientific computing hardware, tools which enable high-throughput workflows, and open well-documented metadata standards to align experimental research better with the fair data stewardship principles. To this end, we introduce paraprobe, an open-source tool for scientific computing and high-throughput studying of point cloud data, here exemplified with APT. We show how to quantify uncertainties while applying several computational geometry, spatial statistics, and clustering tasks for post-processing APT datasets as large as two billion ions. These tools work well in concert with Python and HDF5 to enable several orders of magnitude performance gain, automation, and reproducibility.
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商用原子探针断层扫描系统的硬件和软件进步
影响因子：
2.8
作者：
R. Ulfig;T. Prosa;Yimeng Chen;K. Rice;I. Martin;D. Reinhard;Brian P. Gieser;E. Oltman;D. Lenz;J. Bunton;M. Dyke;T. Kelly;D. Larson
通讯作者：
D. Larson
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DOI：
10.1109/icpp.2008.73
发表时间：
2008
期刊：
2008 37th International Conference on Parallel Processing
影响因子：
--
作者：
S. Seal;M. Moody;A. Ceguerra;S. Ringer;K. Rajan;S. Aluru
通讯作者：
S. Aluru
影响因子：
5
作者：
Gin, S.;Jollivet, P.;Dupuy, L.
通讯作者：
Dupuy, L.
影响因子：
2.8
作者：
Breen, Andrew J.;Babinsky, Katharina;Ringer, Simon P.
通讯作者：
Ringer, Simon P.
使用高斯混合模型检测原子探针数据中的簇
影响因子：
2.8
作者：
J. Zelenty;A. Dahl;J. Hyde;George D. W. Smith;M. Moody
通讯作者：
M. Moody