Machine learning force fields (MLFFs), which employ neural networks to map atomic structures to system energies, effectively combine the high accuracy of first-principles calculation with the computational efficiency of empirical force fields. They are widely used in computational materials simulations. However, the development and application of MLFFs for lithium-ion battery cathode materials remain relatively limited. This is primarily due to the complex electronic structure characteristics of cathode materials and the resulting scarcity of high-quality computational datasets available for force field training. In this work, we develop a multi-fidelity machine learning force field framework to enhance the data efficiency of computational results, which can simultaneously utilize both low-fidelity non-magnetic and high-fidelity magnetic computational datasets of cathode materials for training. Tests conducted on the lithium manganese iron phosphate (LMFP) cathode material system demonstrate the effectiveness of this multi-fidelity approach. This work helps to achieve high-accuracy MLFF training for cathode materials at a lower training dataset cost, and offers new perspectives for applying MLFFs to computational simulations of cathode materials.

翻译：机器学习力场（MLFFs）通过神经网络将原子结构映射至系统能量，有效结合了第一性原理计算的高精度与经验力场的计算效率，广泛应用于计算材料模拟中。然而，针对锂离子电池阴极材料的机器学习力场开发与应用仍相对有限，这主要源于阴极材料复杂的电子结构特性及由此导致的高质量力场训练计算数据集稀缺。本研究开发了一种多保真度机器学习力场框架，以提升计算结果的数���利用效率，该框架可同时利用阴极材料的低保真度非磁性和高保真度磁性计算数据集进行训练。在锂锰铁磷酸盐（LMFP）阴极材料体系上的测试验证了该多保真度方法的有效性。本工作有助于以更低的训练数据集成本实现阴极材料的高精度机器学习力场训练，并为将机器学习力场应用于阴极材料的计算模拟提供了新视角。