发表机构
Rutgers University; NVIDIA Corp; Cornell University（罗格斯大学; 英伟达公司; 康奈尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过LMF理论和朗道理论，揭示短程与长程模型在受限水介电响应上的差异，并推导积分方程，使短程MLIPs结合SPMF理论可线性标度预测非均匀介电函数。
AI 中文摘要
电场介电屏蔽受到界面和受限条件的影响，任何对界面化学的准确描述都必须正确建模非均匀介电张量。然而，具有短程相互作用的模型，如局部机器学习原子间势（MLIPs），正越来越多地用于分子界面模拟，但对其非均匀介电响应缺乏定量理解。为建立这种理解，我们利用局部分子场（LMF）理论提供的框架，量化短程和长程静电在受限水中的作用。短程和长程模型预测出相同的横向介电剖面，但短程模型预测的纵向介电剖面与长程模型存在定性差异。为理解这些差异，我们发展了一个朗道理论，表明长程相互作用使极化涨落变硬，其因子为体相介电常数，并将界面极化关联长度降低一个数量级。LMF理论通过平均场捕捉长程相互作用，能够修正结构和介电响应，但标准涨落关系不再成立。我们推导了LMF理论的涨落关系和积分方程，并展示了它们在预测纵向介电响应方面的准确性。在训练中纳入界面构型的MLIPs本质上学习了界面处的静态平均场修正，因此该积分方程可用于从极化涨落预测其非均匀介电剖面。我们还表明，短程MLIPs可与对称保持平均场（SPMF）理论结合，为长程相互作用提供线性标度替代方案，并预测非均匀介电函数。
英文摘要
Dielectric screening of electric fields is modified by interfaces and confinement, and any accurate description of interfacial chemistry necessitates properly modeling the nonuniform dielectric tensor. However, models with short range interactions, such as local machine learned interatomic potentials (MLIPs), are increasingly used in simulations of molecular interfaces without quantitative understanding of their nonuniform dielectric response. To build this understanding, we use the framework provided by local molecular field (LMF) theory to quantify the roles of short and long range electrostatics in confined water. Short and long range models predict the same transverse dielectric profile but short range models predict qualitatively different longitudinal dielectric profiles than their long range counterparts. To understand these differences, we develop a Landau theory that shows that long range interactions stiffen polarization fluctuations by a factor of the bulk dielectric constant and reduce the interfacial polarization correlation length by an order of magnitude. LMF theory, which captures long range interactions with an averaged field, can correct the structure and dielectric response, but the standard fluctuation relation no longer holds. We derive a fluctuation relation and integral equation for LMF theory and demonstrate their accuracy for predicting longitudinal dielectric response. MLIPs that incorporate interfacial configurations in their training essentially learn a static mean field correction at the interface, such that the integral equation can be used to predict their nonuniform dielectric profile from polarization fluctuations. We also show that short range MLIPs can be combined with symmetry preserving mean field (SPMF) theory to provide a linear-scaling alternative for long range interactions and predicting nonuniform dielectric functions.
Comments16 pages, 10 figures
