AI 中文总结
本文提出一种基于Ising模型的可微平均场框架，从DN、AN、摩尔比和分子大小预测Li+溶剂化壳层组成，在LHCE中实现高精度，并验证了FB作为稀释剂的潜力。
AI 中文摘要
调控Li+溶剂化结构已成为多种锂金属电池（LMB）电解质中一种有前景的设计原则，其中局部高浓度电解质（LHCE）作为领先候选方案脱颖而出。然而，这些电解质的理性设计仍受限于对分子性质如何控制Li+溶剂化壳层组成的定量理解不足。在此，我们引入一个基于Ising模型的平均场建模框架，该框架可根据供体数（DN）、受体数（AN）、摩尔比和分子大小预测溶剂化壳层组成。该框架是端到端可微的，从而能够通过基于梯度的优化直接从分子动力学（MD）溶剂化结构中参数化。聚焦于LHCE，该模型在壳层组成上实现了10.7%的均方根误差（RMSE）和R²=0.87，在自由溶剂比例上实现了2.3%的RMSE，并进一步以MD成本的一小部分复现了真实LHCE电解质中的溶剂化趋势。除LHCE外，我们的模型在其他电解质体系上也表现出良好的泛化能力。对其相互作用项的分析表明，DN主导Li+溶剂化能量学，为基于DN的经验设计规则提供了热力学基础。我们进一步在训练中未包含的LiTFSI/四甘醇二甲醚（G4）体系上展示了该模型的设计实用性。该模型识别出氟苯（FB）为一种有前景的稀释剂，并预测了一个盐浓度窗口，该窗口具有富含阴离子的溶剂化壳层和低自由溶剂，分别有利于稳定的阴离子衍生SEI和高氧化稳定性。这一预测得到了更高保真度MD的验证。这项工作建立了一个可解释的可微框架用于预测Li+溶剂化壳层组成，并具有扩展到其他电解质类别的潜力。
英文摘要
Tailoring Li+ solvation structures has emerged as a promising design principle across multiple Li metal battery (LMB) electrolytes, with localized high concentration electrolytes (LHCE) emerging as a leading candidate. However, rational design of these electrolytes remains limited by a poor quantitative understanding of how molecular properties govern Li+ solvation shell composition. Here we introduce a mean field modeling framework, built on an Ising model, that predicts solvation shell composition from donor number (DN), acceptor number (AN), molar ratio, and molecular size. This framework is end-to-end differentiable, which enables parameterization directly from molecular dynamics (MD) solvation structures via gradient-based optimization. Focusing on LHCE, the model achieves 10.7% RMSE and R2=0.87 for shell composition, 2.3% RMSE on free solvent ratio, and further reproduces solvation trends in real LHCE electrolytes, at a fraction of MD's cost. Our model also shows good generalizability on other electrolyte systems in addition to LHCE. Analysis of its interaction terms shows that DN dominates Li+ solvation energetics, providing a thermodynamic basis for empirical DN-based design rules. We further demonstrate the model's design utility on the LiTFSI/tetraglyme (G4) system absent from training. The model identifies fluorobenzene (FB) as a promising diluent and predicts a salt-concentration window with anion-rich solvation shells and low free solvent, favorable for stable anion-derived SEIs and high oxidative stability, respectively. This prediction is validated by higher-fidelity MD. This work establishes an interpretable differentiable framework for predicting Li+ solvation shell composition, with potential to extend to other electrolyte classes.
