用自监督学习分析三维有序结构的几何特征，无需人工标注数据。
Self-Supervised Learning for Ordered Three-Dimensional Structures
- 基于几何代数构建旋转与排列等变神经网络，处理三维结构。
- 在理想与模拟结构上实现无标签数据下的高精度几何任务求解。
- 适用于材料物理中复杂组装系统的规律挖掘与小样本迁移学习。
近期研究证实，通过自监督任务训练大规模语言模型，并在迁移学习框架下微调以完成新任务，是一种强大方法，可在极少标注数据下构建大参数模型；但该方法应用领域仍有限。本文提出一组适用于大规模研究有序三维结构的几何任务，无需任何人工标注。我们基于几何代数构建深度旋转与排列等变神经网络，在理想化及模拟三维结构上解决这些任务。量化复杂结构中的有序性是材料物理领域长期挑战；这些模型可从学习任务中提取洞察，无需额外修改，或通过迁移学习以少量标注数据解决新任务。
原文摘要 · Abstract (English)
Recent work has proven that training large language models with self-supervised tasks and fine-tuning these models to complete new tasks in a transfer learning setting is a powerful idea, enabling the creation of models with many parameters, even with little labeled data; however, the number of domains that have harnessed these advancements has been limited. In this work, we formulate a set of geometric tasks suitable for the large-scale study of ordered three-dimensional structures, without requiring any human intervention in data labeling. We build deep rotation- and permutation-equivariant neural networks based on geometric algebra and use them to solve these tasks on both idealized and simulated three-dimensional structures. Quantifying order in complex-structured assemblies remains a long-standing challenge in materials physics; these models can elucidate the behavior of real self-assembling systems in a variety of ways, from distilling insights from learned tasks without further modification to solving new tasks with smaller amounts of labeled data via transfer learning.
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