自洽静电机器学习互原子势的设计空间
Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials
AI总结：
本文提出一种框架，将现有模型视为密度泛函理论的粗粒度近似，揭示了自洽静电MLIPs的设计空间关键选择，通过MACE架构和共享电荷密度表示进行比较，并在金属-水界面和硅 dioxide 中的带电空位测试案例中评估模型性能。
AI中文摘要：
机器学习互原子势（MLIPs）已成为原子模拟中广泛应用的工具。在该领域大部分历史中，最常用的架构基于短程原子能量贡献，局部性假设仍然存在于许多现代基础模型中。虽然这种方法在许多应用中实现了高效和准确的建模，但对需要长程静电学、电荷转移或诱导极化的系统存在内在限制。越来越多的工作提出了扩展方法，包括局部预测原子电荷到自洽模型。尽管这些模型在特定示例中表现出色，但其底层假设和基本限制尚未充分理解。本文提出了一种框架，通过将现有模型视为密度泛函理论（DFT）的粗粒度近似来处理MLIPs中的静电学。这种视角明确说明了所涉及的近似，澄清了所学量的物理意义，并揭示了先前提出模型之间的联系和等价性。利用这种形式化，我们识别了定义更广泛自洽静电MLIPs设计空间的关键设计选择。我们使用MACE架构和共享的电荷密度表示来实现该空间中的显著点，从而实现不同方法的受控比较。最后，我们在这两个有指导性的测试案例上评估这些模型：金属-水界面，用于探测导电和绝缘系统的对比静电响应，以及硅 dioxide 中的带电空位。我们的结果突显了现有方法的局限性，并展示了更具有表达力的自洽模型如何解决失败问题。
英文摘要：
Machine learning interatomic potentials (MLIPs) have become widely used tools in atomistic simulations. For much of the history of this field, the most commonly employed architectures were based on short-ranged atomic energy contributions, and the assumption of locality still persists in many modern foundation models. While this approach has enabled efficient and accurate modelling for many use cases, it poses intrinsic limitations for systems where long-range electrostatics, charge transfer, or induced polarization play a central role. A growing body of work has proposed extensions that incorporate electrostatic effects, ranging from locally predicted atomic charges to self-consistent models. While these models have demonstrated success for specific examples, their underlying assumptions, and fundamental limitations are not yet well understood. In this work, we present a framework for treating electrostatics in MLIPs by viewing existing models as coarse-grained approximations to density functional theory (DFT). This perspective makes explicit the approximations involved, clarifies the physical meaning of the learned quantities, and reveals connections and equivalences between several previously proposed models. Using this formalism, we identify key design choices that define a broader design space of self-consistent electrostatic MLIPs. We implement salient points in this space using the MACE architecture and a shared representation of the charge density, enabling controlled comparisons between different approaches. Finally, we evaluate these models on two instructive test cases: metal-water interfaces, which probe the contrasting electrostatic response of conducting and insulating systems, and charged vacancies in silicon dioxide. Our results highlight the limitations of existing approaches and demonstrate how more expressive self-consistent models are needed to resolve failures.
