AlphaNet提升原子间势能计算的精度与效率，助力材料与催化设计。
AlphaNet: Scaling Up Local-frame-based Atomistic Interatomic Potential
- 基于可学习几何变换的局部坐标系，增强原子环境表征能力
- 在多个大规模数据集上实现能量与力预测的顶尖性能
- 适合需要高精度、高扩展性的多尺度材料模拟研究者
分子动力学模拟需兼具高精度与高可扩展性以应对催化与材料设计中的重大挑战。为此，我们提出AlphaNet，一种基于局部坐标系的等变模型，在保持计算高效的同时显著提升原子相互作用预测精度。通过构建可学习几何变换的等变局部框架，AlphaNet增强了原子环境的表征能力，在能量与力预测方面达到当前最优水平。在涵盖分子反应、晶体稳定性与表面催化的大型数据集（Matbench Discovery 和 OC2M）上的广泛测试表明，其性能优于现有神经网络原子势函数，且在不同系统尺寸和相互作用类型下均具备良好可扩展性。精度、效率与迁移能力的协同使AlphaNet成为建模多尺度现象、解析催化与功能界面动力学的变革性工具，对加速复杂分子体系与功能材料的发现具有重要意义。
原文摘要 · Abstract (English)
Molecular dynamics simulations demand an unprecedented combination of accuracy and scalability to tackle grand challenges in catalysis and materials design. To bridge this gap, we present AlphaNet, a local-frame-based equivariant model that simultaneously improves computational efficiency and predictive precision for interatomic interactions. By constructing equivariant local frames with learnable geometric transitions, AlphaNet encodes atomic environments with enhanced representational capacity, achieving state-of-the-art accuracy in energy and force predictions. Extensive benchmarks on large-scale datasets spanning molecular reactions, crystal stability, and surface catalysis (Matbench Discovery and OC2M) demonstrate its superior performance over existing neural network interatomic potentials while ensuring scalability across diverse system sizes with varying types of interatomic interactions. The synergy of accuracy, efficiency, and transferability positions AlphaNet as a transformative tool for modeling multiscale phenomena, decoding dynamics in catalysis and functional interfaces, with direct implications for accelerating the discovery of complex molecular systems and functional materials.
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