利用带不确定性量化的免迭代深度生成模型实现可扩展的晶体结构弛豫
Scalable Crystal Structure Relaxation Using an Iteration-Free Deep Generative Model with Uncertainty Quantification
AI总结：
本文提出免迭代深度生成模型DeepRelax，可直接从非平衡结构预测晶体平衡结构，实现毫秒级弛豫，并通过不确定性量化提升可信度，适用于大规模材料筛选。
摘要：
在计算分子与材料科学中，确定平衡结构是后续准确性质计算的关键第一步。然而，近期数百万种新晶体和复杂扭转结构的发现，对传统的计算方法——无论是从头计算方法还是基于机器学习的方法——都构成了挑战，因为这些方法依赖计算密集的迭代过程。为解决这些可扩展性问题，本文引入了DeepRelax，这是一种深度生成模型，能够快速且无需迭代地执行几何晶体结构弛豫。DeepRelax学习平衡结构分布，使其能够从未弛豫结构直接预测弛豫后的结构。每个结构的弛豫可在毫秒级完成，再结合并行处理的可扩展性，使DeepRelax特别适用于大规模虚拟筛选。我们通过将DeepRelax应用于五个不同的数据库来证明其可靠性和鲁棒性，这些数据库包括氧化物、Materials Project、二维材料、范德瓦尔斯晶体以及含点缺陷的晶体。DeepRelax始终表现出高精度和高效率，并通过密度泛函理论计算得到验证。最后，我们通过集成不确定性量化来增强其可信度。这项工作显著加速了计算工作流程，为材料发现提供了一种鲁棒且可信的机器学习方法，并推动了AI for Science的应用。DeepRelax的代码可在https://github.com/Shen-Group/DeepRelax获取。
英文摘要：
In computational molecular and materials science, determining equilibrium structures is the crucial first step for accurate subsequent property calculations. However, the recent discovery of millions of new crystals and complex twisted structures has challenged traditional computational methods, both ab initio and machine-learning-based, due to their computationally intensive iterative processes. To address these scalability issues, here we introduce DeepRelax, a deep generative model capable of performing geometric crystal structure relaxation rapidly and without iterations. DeepRelax learns the equilibrium structural distribution, enabling it to predict relaxed structures directly from their unrelaxed ones. The ability to perform structural relaxation at the millisecond level per structure, combined with the scalability of parallel processing, makes DeepRelax particularly useful for large-scale virtual screening. We demonstrate DeepRelax's reliability and robustness by applying it to five diverse databases, including oxides, Materials Project, two-dimensional materials, van der Waals crystals, and crystals with point defects. DeepRelax consistently shows high accuracy and efficiency, validated by density functional theory calculations. Finally, we enhance its trustworthiness by integrating uncertainty quantification. This work significantly accelerates computational workflows, offering a robust and trustworthy machine-learning method for material discovery and advancing the application of AI for science. Code for DeepRelax is available at https://github.com/Shen-Group/DeepRelax.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
