混合不变与等变架构，提升材料力场模型精度与速度
A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures
- 融合不变与等变消息传递层，兼顾物理约束与计算效率
- 在基准测试中表现领先，计算速度显著优于现有模型
- 适合需要高精度与高效能的材料发现研究者使用
机器学习原子间势（MLIP）可预测材料的能量、力和应力，支持多种下游材料发现任务。MLIP设计中的关键权衡在于不变与等变架构之间的取舍：不变模型计算高效但性能受限，尤其在高阶输出预测时；等变模型能捕捉高阶对称性，但计算成本高。本文提出HIENet，一种混合不变-等变材料原子间势模型，集成不变与等变消息传递层，同时严格满足关键物理约束。实验结果表明，HIENet在常见基准和下游材料发现任务中均达到当前最优性能，并显著提升计算速度。
原文摘要 · Abstract (English)
Machine learning interatomic potentials (MLIPs) can predict energy, force, and stress of materials and enable a wide range of downstream discovery tasks. A key design choice in MLIPs involves the trade-off between invariant and equivariant architectures. Invariant models offer computational efficiency but may not perform as well, especially when predicting high-order outputs. In contrast, equivariant models can capture high-order symmetries, but are computationally expensive. In this work, we propose HIENet, a hybrid invariant-equivariant materials interatomic potential model that integrates both invariant and equivariant message passing layers, while provably satisfying key physical constraints. HIENet achieves state-of-the-art performance with considerable computational speedups over prior models. Experimental results on both common benchmarks and downstream materials discovery tasks demonstrate the efficiency and effectiveness of HIENet.
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