Crystal structure identification with 3D convolutional neural networks with application to high-pressure phase transitions in SiO2

IF 1.9 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY
Linus C Erhard, Daniel Utt, Arne J Klomp and Karsten Albe
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引用次数: 0

Abstract

Efficient, reliable and easy-to-use structure recognition of atomic environments is essential for the analysis of atomic scale computer simulations. In this work, we train two neuronal network (NN) architectures, namely PointNet and dynamic graph convolutional NN (DG-CNN) using different hyperparameters and training regimes to assess their performance in structure identification tasks of atomistic structure data. We show benchmarks on simple crystal structures, where we can compare against established methods. The approach is subsequently extended to structurally more complex SiO2 phases. By making use of this structure recognition tool, we are able to achieve a deeper understanding of the crystallization process in amorphous SiO2 under shock compression. Lastly, we show how the NN based structure identification workflows can be integrated into OVITO using its python interface.
利用三维卷积神经网络识别晶体结构,并将其应用于二氧化硅的高压相变
高效、可靠且易于使用的原子环境结构识别对于原子尺度计算机模拟分析至关重要。在这项工作中,我们使用不同的超参数和训练机制训练两种神经元网络(NN)架构,即 PointNet 和动态图卷积 NN(DG-CNN),以评估它们在原子结构数据的结构识别任务中的性能。我们展示了简单晶体结构的基准测试,并与已有方法进行了比较。随后,我们将该方法扩展到结构更为复杂的 SiO2 相。利用这种结构识别工具,我们能够更深入地了解冲击压缩下非晶态二氧化硅的结晶过程。最后,我们展示了如何利用 Python 界面将基于 NN 的结构识别工作流程集成到 OVITO 中。
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来源期刊
CiteScore
3.30
自引率
5.60%
发文量
96
审稿时长
1.7 months
期刊介绍: Serving the multidisciplinary materials community, the journal aims to publish new research work that advances the understanding and prediction of material behaviour at scales from atomistic to macroscopic through modelling and simulation. Subject coverage: Modelling and/or simulation across materials science that emphasizes fundamental materials issues advancing the understanding and prediction of material behaviour. Interdisciplinary research that tackles challenging and complex materials problems where the governing phenomena may span different scales of materials behaviour, with an emphasis on the development of quantitative approaches to explain and predict experimental observations. Material processing that advances the fundamental materials science and engineering underpinning the connection between processing and properties. Covering all classes of materials, and mechanical, microstructural, electronic, chemical, biological, and optical properties.
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