先进的机器学习原子间潜力用于在大型Si@C核心外阳极中加速化动态的原子模拟
Yujie Liao1, Pengfei Suo2, Changhao Wang1,3
1Zhejiang Laboratory, Hangzhou 311100, P. R. China.
ACS applied materials & interfaces
|December 31, 2025
概括
开发用于离子电池的先进碳阳极需要了解化. 一个新的机器学习模型加速了原子学模拟,揭示了~4纳米的碳层,以优化降低体积膨胀,以提高电池稳定性.
科学领域:
- 材料科学 材料科学 材料科学
- 电化学 电化学 电化学
- 计算化学的计算化学
背景情况:
- 阳极为离子电池提供高容量,但在化过程中遭受大量体积膨胀,限制周期寿命.
- 了解中的化原子级动态对于设计稳定的阳极至关重要.
研究的目的:
- 开发一个高效的计算模型来模拟碳阳极中的化动态.
- 调查碳厚度对体积膨胀和分布的作用.
主要方法:
- 开发一个神经进化潜力 (NEP) 模型用于原子模拟.
- 使用直接采样策略来优化训练数据大小.
- 在化过程中对碳核心外结构进行大规模模拟.
主要成果:
- 在近密度函数理论准确度的基础上,NEP模型实现了超过ab initio分子动态的70,000倍的加速度.
- 发现~4纳米的碳层可以将体积膨胀最小化到<1%并使内部膨胀/外部锁定机制成为可能.
- 该模型准确地预测了原子力,辐射分布函数和在各种结构配置中的扩散性.
结论:
- 机器学习的原子间潜力是弥合材料设计中的计算可行性和原子精度的强大工具.
- 一个~4纳米的碳外是抑制碳阳极体积膨胀的最佳,增强循环稳定性.
- 这项工作为设计下一代高性能碳阳极提供了基本的见解.
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