npj Computational Materials

Construction of ground-state preserving sparse lattice models for predictive materials simulations(构建能保持基态稀有晶格的模型以预测模拟材料)
Wenxuan Huang, Alexander Urban, Ziqin Rong, Zhiwei Ding, Chuan Luo & Gerbrand Ceder
npj Computational Materials 3:30 (2017)
doi:10.1038/s41524-017-0032-0
Published online:07 August 2017
Abstract| Full Text | PDF OPEN

摘要:基于第一原理的集群扩展模型是根据从头算热力学理论研究晶体混合的主要方法，可以预测相图和基态。然而，尽管近来取得了一些进展，由于默认参数会导致各种各样的问题，仍需要耗时费力地手动调整参数才能保证构建模型的准确性。本研究提出了一种系统的、数学上可靠的方法，确保基态与参考数据一致的簇扩展模型。基于新开发的压缩感知途径方法，我们建立了集群扩展模型，并采用二次规划对模型参数加以约束。对于锂离子电池阴极有关的两种锂过渡金属氧化物（Li2xFe2(1-x)O2和Li2xTi2(1-x)O2），我们构建了极具挑战的、带有压缩感知能力的簇扩展模型，证明了本方法的强大实用性。研究证明，我们的方法不仅保证了为模型构建而使用的参考结构集的基态准确，而且通过快速收敛迭代保证了样本之外尺寸较大的超胞基态准确。本法提供了一种通用工具，可用来构建强实用的、压缩的、受约束的、有预测功能的物理模型。　　

Abstract: First-principles based cluster expansion models are the dominant approach inab initiothermodynamics of crystalline mixtures enabling the prediction of phase diagrams and novel ground states. However, despite recent advances, the construction of accurate models still requires a careful and time-consuming manual parameter tuning process for ground-state preservation, since this property is not guaranteed by default. In this paper, we present a systematic and mathematically sound method to obtain cluster expansion models that are guaranteed to preserve the ground states of their reference data. The method builds on the recently introduced compressive sensing paradigm for cluster expansion and employs quadratic programming to impose constraints on the model parameters. The robustness of our methodology is illustrated for two lithium transition metal oxides with relevance for Li-ion battery cathodes, i.e., Li2x Fe2(1−x)O2 and Li2xTi2(1−x)O2, for which the construction of cluster expansion models with compressive sensing alone has proven to be challenging. We demonstrate that our method not only guarantees ground-state preservation on the set of reference structures used for the model construction, but also show that out-of-sample ground-state preservation up to relatively large supercell size is achievable through a rapidly converging iterative refinement. This method provides a general tool for building robust, compressed and constrained physical models with predictive power.

Editorial Summary

Materials simulations: Constructing models guaranteed to preserve the ground states(材料模拟：构建能确保维持基态的模型)

该研究开发了用于材料模拟、不需手动调整输入参数的方法。第一原理密度泛函理论计算是计算材料学研究中最常用的一种工具，但难于应用到含数千个原子的大型结构模拟。这样的系统通常使用集群扩展模型来模拟，但存在一个问题：需要手动调整参数以保持基态——这个调整很重要，因这一调整通常决定了材料的属性。现在来自美国麻省理工学院、加州大学伯克利分校和劳伦斯伯克利国家实验室的Gerbrand Ceder教授（美国2017年新科工程院院士）领导的国际研究团队，提出了构建集群扩展模型的方法，可以保证基态准确而无需手动调整参数。

A method has been developed for performing materials simulations without needing to perform manual parameter tuning for the ground-state. First-principles density functional theory calculations are one of the most commonly used tools for computational materials science research but they cannot easily be applied to large structures that contain many thousands of atoms.In such systems, cluster expansion models are often used but they have a problem: manual parameter tuning is required to preserve the ground-state --- important as this usually governs the materials properties. An international team of researchers led by Gerbrand Ceder from Massachusetts Institute of Technology, the University of California Berkeley and Lawrence Berkeley National Laboratory now present a procedure for constructing cluster expansion models that can preserve the ground states without any need for tuning.