Crystal property prediction is a crucial aspect of developing novel materials. However, there are two technical challenges to be addressed for speeding up the investigation of crystals. First, labeling crystal properties is intrinsically difficult due to the high cost and time involved in physical simulations or lab experiments. Second, crystals adhere to a specific quantum chemical principle known as periodic invariance, which is often not captured by existing machine learning methods. To overcome these challenges, we propose the crystal-specific pre-training framework for learning crystal representations with self-supervision. The framework designs a mutex mask strategy for enhancing representation learning so as to alleviate the limited labels available for crystal property prediction. Moreover, we take into account the specific periodic invariance in crystal structures by developing a periodic invariance multi-graph module and periodic attribute learning within our framework. This framework has been tested on eight different tasks. The experimental results on these tasks show that the framework achieves promising prediction performance and is able to outperform recent strong baselines.

翻译：晶体性质预测是新型材料开发中的关键环节。然而，加速晶体研究面临两项技术挑战：首先，由于物理模拟或实验室实验所需的高昂成本与时间成本，晶体性质标注本身存在固有困难；其次，晶体遵循名为周期性不变性的特定量子化学原理，而现有机器学习方法往往未能捕捉这一特性。为克服这些挑战，我们提出了一种面向晶体表征的自监督学习晶体特异性预训练框架。该框架设计了互斥掩码策略以增强表征学习，从而缓解晶体性质预测中可用标签有限的问题。此外，通过开发周期性不变性多图模块并在框架内引入周期性属性学习，我们充分考虑了晶体结构中的特异性周期性不变性。该框架已在八个不同任务上完成测试，实验结果表明该框架具有优异的预测性能，能够超越近期强基线方法。