用坐标直接建模原子交互，高效预测点缺陷能量与受力。
ADAPT: Lightweight, Long-Range Machine Learning Force Fields Without Graphs
- 抛弃图结构，直接用坐标和Transformer处理原子对相互作用。
- 在硅点缺陷数据上，力与能量误差降低33%，计算开销更小。
- 适合需长程作用的材料缺陷模拟，尤其适合高通量计算。
点缺陷在决定材料性质中起核心作用。第一性原理方法虽广泛用于计算缺陷能量与结构，但计算成本高，难以大规模应用。机器学习势能模型（MLFF）成为加速结构弛豫的可行替代方案。然而，现有主流方法基于图神经网络（GNN），存在过平滑问题，且对长程相互作用表征不佳，这在点缺陷建模中尤为显著。为此，我们提出加速深度原子势能变换器（ADAPT），采用直接的空间坐标表示，显式建模所有原子对之间的相互作用。原子被视作序列中的“令牌”，通过Transformer编码器捕捉其相互作用。在硅点缺陷数据集上的实验表明，相比最先进的基于GNN的模型，ADAPT在力和能量预测误差上均降低约33%，同时计算开销仅为后者的极小部分。
原文摘要 · Abstract (English)
Point defects play a central role in driving the properties of materials. First-principles methods are widely used to compute defect energetics and structures, including at scale for high-throughput defect databases. However, these methods are computationally expensive, making machine-learning force fields (MLFFs) an attractive alternative for accelerating structural relaxations. Most existing MLFFs are based on graph neural networks (GNNs), which can suffer from oversmoothing and poor representation of long-range interactions. Both of these issues are especially of concern when modeling point defects. To address these challenges, we introduce the Accelerated Deep Atomic Potential Transformer (ADAPT), an MLFF that replaces graph representations with a direct coordinates-in-space formulation and explicitly considers all pairwise atomic interactions. Atoms are treated as tokens, with a Transformer encoder modeling their interactions. Applied to a dataset of silicon point defects, ADAPT achieves a roughly 33 percent reduction in both force and energy prediction errors relative to a state-of-the-art GNN-based model, while requiring only a fraction of the computational cost.
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