让神经网络势函数学会处理自旋自由度，提升过渡金属氧化物模拟精度
SpinMultiNet: Neural Network Potential Incorporating Spin Degrees of Freedom with Multi-Task Learning
- 通过多任务学习融合自旋自由度，无需精确初始自旋值
- 准确复现超交换作用导致的自旋构型能量顺序和岩盐结构畸变
- 适合研究磁性材料与自旋相关性质的大规模材料模拟
神经网络势函数（NNPs）因其加速密度泛函理论（DFT）计算的潜力而受到广泛关注。然而，传统NNP模型通常未考虑自旋自由度，限制了其在自旋状态显著影响材料性质体系（如过渡金属氧化物）中的应用。本文提出SpinMultiNet，一种通过多任务学习整合自旋自由度的新型NNP模型。该模型不依赖DFT提供的正确自旋值，而是以初始自旋估计为输入，利用多任务学习优化自旋隐含表示，同时保持$E(3)$和时间反演等变性。在过渡金属氧化物数据集上的验证表明，SpinMultiNet具有高预测精度，成功复现由超交换作用引起的稳定自旋构型能量排序，并准确捕捉岩盐结构的菱方畸变。这些结果为考虑自旋自由度的材料模拟开辟新路径，有望应用于各类材料体系的大规模模拟，包括磁性材料。
原文摘要 · Abstract (English)
Neural Network Potentials (NNPs) have attracted significant attention as a method for accelerating density functional theory (DFT) calculations. However, conventional NNP models typically do not incorporate spin degrees of freedom, limiting their applicability to systems where spin states critically influence material properties, such as transition metal oxides. This study introduces SpinMultiNet, a novel NNP model that integrates spin degrees of freedom through multi-task learning. SpinMultiNet achieves accurate predictions without relying on correct spin values obtained from DFT calculations. Instead, it utilizes initial spin estimates as input and leverages multi-task learning to optimize the spin latent representation while maintaining both $E(3)$ and time-reversal equivariance. Validation on a dataset of transition metal oxides demonstrates the high predictive accuracy of SpinMultiNet. The model successfully reproduces the energy ordering of stable spin configurations originating from superexchange interactions and accurately captures the rhombohedral distortion of the rocksalt structure. These results pave the way for new possibilities in materials simulations that consider spin degrees of freedom, promising future applications in large-scale simulations of various material systems, including magnetic materials.
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