Machine-learning interatomic potentials: A new avenue towards modelling of energy-storage materials

该项目的目标是为三元Li-Si-O系统开发机器学习原子间势，并将其应用于分子动力学模拟，以提高对一氧化硅微观结构和材料特性的理解，特别关注（脱）锂行为。在此过程中，我们将对不同类别的机器学习（ML）潜力进行基准测试，评估它们的计算速度，数值精度和可转移性（外推能力），并最终相应地对它们进行分类。第一步，我们将最近发展的SiO_2的高斯近似势（GAP）推广到全二元Si-O系统（SiO_X）。我们将使用和扩展从密度泛函理论（DFT）计算获得的训练数据库。使用这个扩展的数据集，我们将训练替代的ML势，例如神经网络势（NNP），矩张量势（MTP）和原子簇扩展势（ACE）。这将允许在不同ML方法之间进行无偏比较，并指导我们选择三元体系。此外，该比较将提供各种潜在类别性能的无偏见比较。使用Si-O子系统的这种新的潜力，我们将生成原始一氧化硅的不同原子模型。我们的目标是更好地了解这些材料的微观/纳米结构以及相关的机械和热性能。然后将使用全部Li-Si-O电势来研究锂化（脱锂）。具体而言，模拟将使我们能够识别Li扩散途径和迁移率，可逆的Li存储位点和导致形成第二相的不可逆副反应。了解这些过程可能最终有助于指导实验，以提高循环寿命和倍率性能的一氧化硅阳极纳米结构的有针对性的设计。

The objective of this project is to develop machine learning interatomic potentials for the ternary Li-Si-O system and apply them in molecular-dynamics simulations to improve the understanding of the microstructure and material properties of silicon monoxide with particular focus on the (de)lithiation behaviour. In doing so, we will benchmark different classes of machine learning (ML) potentials, evaluate their computational speed, numerical accuracy and transferability (extrapolation capabilities and finally classify them, accordingly. In a first step, we will extend our recently-developed Gaussian approximation potential (GAP) for SiO2 to the full binary Si-O system (SiO_X ). We will use and extend our training database obtained from density functional theory (DFT) calculations. Using this extended dataset we will train alternative ML potentials, such as neural network potentials (NNP), moment tensor potentials (MTP) and atomic cluster expansion potentials (ACE). This will allow for a non-biased comparison between different ML approaches and guide our selection for the ternary system. Moreover, the comparison will provide a non-biased comparison of the various potential classes performance- Using the this novel potential for the Si-O subsystem, we will then generate different atomistic models for pristine silicon monoxide. The goal is to better understand the micro-/nanostructure of these materials and related mechanical and thermal properties. The full Li-Si-O potential will then be used to study (de)lithiation. Specifically, the simulations will allow us to identify Li diffusion pathways and mobility, reversible Li storage sites and irreversible side reactions leading to formation of secondary phases. Understanding these processes may eventually help guiding experiments to improve cycle life and rate capability by targeted design of silicon-monoxide anode nanostructure.