Low-index mesoscopic surface reconstructions of Au surfaces using Bayesian force fields

Metal surfaces have long been known to reconstruct, significantly influencing their structural and catalytic properties. Many key mechanistic aspects of these subtle transformations remain poorly understood due to limitations of previous simulation approaches. Using active learning of Bayesian machine-learned force fields trained from ab initio calculations, we enable large-scale molecular dynamics simulations to describe the thermodynamics and time evolution of the low-index mesoscopic surface reconstructions of Au (e.g., the Au(111)-`Herringbone,' Au(110)-(1x2)-`Missing-Row,' and Au(100)-`Quasi-Hexagonal' reconstructions). This capability yields direct atomistic understanding of the dynamic emergence of these surface states from their initial facets, providing previously inaccessible information such as nucleation kinetics and a complete mechanistic interpretation of reconstruction under the effects of strain and local deviations from the original stoichiometry. We successfully reproduce previous experimental observations of reconstructions on pristine surfaces and provide quantitative predictions of the emergence of spinodal decomposition and localized reconstruction in response to strain at non-ideal stoichiometries. A unified mechanistic explanation is presented of the kinetic and thermodynamic factors driving surface reconstruction. Furthermore, we study surface reconstructions on Au nanoparticles, where characteristic (111) and (100) reconstructions spontaneously appear on a variety of high-symmetry particle morphologies. The training data, MLFF, and subsequent simulations are provided for reproducibility.

金属表面长期以来以其重构能力而著称，这一特性对其结构和催化性能产生了显著影响。由于先前模拟方法的局限性，这些微妙转变的关键机制方面至今仍理解不深。通过主动学习贝叶斯机器学习力场，该力场由从头计算训练而来，我们得以开展大规模分子动力学模拟，以描述金（Au）低指数介观表面重构（例如，Au(111)-`Herringbone'、Au(110)-(1x2)-`Missing-Row'和Au(100)-`Quasi-Hexagonal'重构）的热力学和时间演化。这一能力为我们提供了直接原子级别的对表面状态动态形成的理解，揭示了之前无法获取的信息，如成核动力学和重构在应变及局部偏离原始化学计量比作用下的完整机制解释。我们成功再现了原始表面上的重构实验观测结果，并提供了对非理想化学计量比应变下相分离和局域重构出现的定量预测。对于驱动表面重构的动力学和热力学因素，我们提出了统一的机制解释。此外，我们还研究了金纳米粒子上的表面重构，其中特征性的（111）和（100）重构在各种高对称粒子形态上自发出现。为保障可重复性，我们提供了训练数据、机器学习力场（MLFF）以及后续模拟。