硬碳的原子级结构生成与神经网络筛选以识别高容量钠存储材料
Atomistic Structure Generation and Neural-Network Screening of Hard Carbons to Identify High-Capacity Sodium Storage
AI总结：
本研究结合通用机器学习原子间势与RAFFLE框架生成13096个硬碳模型，训练神经网络代理筛选出容量超800 mAh g⁻¹的高容量钠存储候选材料，建立了硬碳微观结构与钠存储性能的关联。
AI中文摘要：
硬碳是锂离子电池公认的负极材料，也是钠离子电池的核心候选材料，但其电化学性能受石墨 domains、缺陷和纳米孔构成的异质网络控制，传统原子级方法无法在所需长度尺度上对其建模。我们将通用机器学习原子间势与RAFFLE结构生成框架结合，构建了13096个真实硬碳模型，包含多达4378个原子，匹配实验测量的密度、孔隙率和sp²/sp³键合比例。对代表性结构进行的 explicit 钠嵌入重现了从倾斜到平台的特征电压曲线，表明容量随碳密度降低和孔隙率增加而升高。为筛选全部库，我们训练了轻量级神经网络代理模型，利用冻结的通用势描述符和几何孔隙特征直接从宿主预测容量。该代理模型识别出超过800 mAh g⁻¹的高容量候选材料，已通过完整嵌入计算验证。该可扩展框架将硬碳微观结构与钠存储性能关联，为高容量负极提供原子级设计原则；更广泛地说，该工作流程可实现对合成依赖的无定形微观结构的系统探索，包括前驱体化学、热解、杂原子掺杂和孔隙工程，为原子级信息指导的硬碳设计提供了途径。
英文摘要：
Hard carbons are established anodes for lithium-ion batteries and leading candidates for sodium-ion batteries, yet their electrochemical performance is governed by a heterogeneous network of graphitic domains, defects, and nanopores that conventional atomistic methods cannot model at the required length scales. We combine universal machine-learned interatomic potentials with the RAFFLE structure-generation framework to construct 13,096 realistic hard carbon models containing up to 4,378 atoms, matching experimentally measured densities, porosities, and sp$^2$/sp$^3$ bonding fractions. Explicit sodium intercalation of representative structures reproduces the characteristic sloping-to-plateau voltage profiles, revealing that capacity increases with decreasing carbon density and increasing porosity. To screen the full library, we train a lightweight neural-network surrogate that predicts capacity directly from the host using frozen universal-potential descriptors augmented by geometric void features. The surrogate identifies high-capacity candidates exceeding 800 mAh g$^{-1}$, which are validated by full intercalation calculations. This scalable framework links hard carbon microstructure to sodium-storage performance and provides atomistic design principles for high-capacity anodes. More broadly, the workflow enables systematic exploration of synthesis-dependent amorphous microstructures, including precursor chemistry, pyrolysis, heteroatom doping, and pore engineering, providing a route toward atomistically informed hard carbon design.
