标题： 基于机器学习力场的分子模拟研究硬碳负极在钠离子电池中的钠存储机制 显示英文标题

标题： Theoretical Studies on Sodium Storage Mechanism in Hard Carbon Anodes of Sodium-Ion Batteries: Molecular Simulations Based on Machine Learning Force Fields

摘要： 钠离子电池（SIBs）近年来因其低成本、丰富的钠资源和优异的循环性能，成为一种有前景的锂离子电池（LIBs）替代品，受到了广泛关注。 硬碳材料以其高比容量、优异的循环稳定性和低成本而著称，已成为SIB负极的潜在候选材料。 然而，硬碳负极中的钠存储机制仍高度复杂，尤其是在无序结构中，尚未完全理解。 为解决这一问题，我们结合了相对机器学习力场（MLFFs）和多尺度模拟技术，系统地研究了硬碳中的钠存储行为。 通过整合模拟，本研究对钠的吸附、插层和填充机制进行了详细探索。 高精度、大尺度的模拟揭示了钠离子在硬碳中的动态行为和分布模式。 这些发现不仅加深了我们对硬碳负极中钠存储机制的理解，还为优化未来的SIB设计提供了理论基础，同时引入了新的模拟方法和技术框架以提高电池性能。 显示英文摘要

摘要： Sodium-ion batteries (SIBs) have garnered significant attention in recent years as a promising alternative to lithium-ion batteries (LIBs) due to their low cost, abundant sodium resources, and excellent cycling performance. Hard carbon materials, characterized by their high specific capacity, outstanding cycling stability, and low cost, have emerged as potential candidates for SIB anodes. However, the sodium storage mechanism in hard carbon anodes remains highly complex, especially in disordered structures, and is yet to be fully understood. To address this, we employed relative machine learning force fields (MLFFs) in conjunction with multiscale simulation techniques to systematically investigate the sodium storage behavior in hard carbon. By integrating simulations, this study provides a detailed exploration of sodium adsorption, intercalation, and filling mechanisms. High-precision, large-scale simulations reveal the dynamic behavior and distribution patterns of sodium ions in hard carbon. The findings not only deepen our understanding of sodium storage mechanisms in hard carbon anodes, but also offer a theoretical foundation for optimizing future SIB designs, while introducing novel simulation methodologies and technical frameworks to enhance battery performance.