从评估到设计：利用势能面平滑度指标指导机器学习原子间势架构
From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
AI总结：
提出键平滑度表征测试（BSCT）作为高效评估机器学习原子间势（MLIP）势能面平滑度的指标，并与分子动力学稳定性强相关，同时指导模型设计以减少伪影。
摘要：
机器学习原子间势（MLIP）有时无法再现量子势能面（PES）的物理平滑性，导致下游模拟中出现标准能量和力回归评估无法捕捉的错误行为。现有评估方法（如微正则分子动力学（MD））计算成本高且主要探测近平衡态。为改进MLIP的评估指标，我们引入键平滑度表征测试（BSCT）。该高效基准通过受控键变形探测PES，检测近平衡和远离平衡态的非平滑性，包括不连续性、人工极小值和虚假力。我们证明BSCT与MD稳定性强相关，而成本仅为MD的一小部分。为展示BSCT如何指导迭代模型设计，我们利用无约束Transformer主干作为测试平台，说明如何通过改进（如新的可微$k$-最近邻算法和温度控制注意力）减少指标识别的伪影。通过基于BSCT系统优化模型设计，所得MLIP同时实现了低传统E/F回归误差、稳定的MD模拟和鲁棒的原子性质预测。我们的结果将BSCT确立为从业者评估MLIP实用性的验证指标，以及“循环内”模型设计代理，提醒MLIP开发者注意当前MLIP基准无法高效评估的物理挑战。BSCT数据集和评估可在https://github.com/ryanliu30/bsct.git获取。
英文摘要：
Machine Learning Interatomic Potentials (MLIPs) sometimes fail to reproduce the physical smoothness of the quantum potential energy surface (PES), leading to erroneous behavior in downstream simulations that standard energy and force regression evaluations can miss. Existing evaluations, such as microcanonical molecular dynamics (MD), are computationally expensive and primarily probe near-equilibrium states. To improve evaluation metrics for MLIPs, we introduce the Bond Smoothness Characterization Test (BSCT). This efficient benchmark probes the PES via controlled bond deformations and detects non-smoothness, including discontinuities, artificial minima, and spurious forces, both near and far from equilibrium. We show that BSCT correlates strongly with MD stability while requiring a fraction of the cost of MD. To demonstrate how BSCT can guide iterative model design, we utilize an unconstrained Transformer backbone as a testbed, illustrating how refinements such as a new differentiable $k$-nearest neighbors algorithm and temperature-controlled attention reduce artifacts identified by our metric. By optimizing model design systematically based on BSCT, the resulting MLIP simultaneously achieves a low conventional E/F regression error, stable MD simulations, and robust atomistic property predictions. Our results establish BSCT as both a validation metric for practitioners to assess MLIP utility and as an "in-the-loop" model design proxy that alerts MLIP developers to physical challenges that cannot be efficiently evaluated by current MLIP benchmarks. The BSCT dataset and evaluation are available on https://github.com/ryanliu30/bsct.git
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