发表机构
Los Alamos National Laboratory; Center for Integrated Nanotechnologies, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室; 洛斯阿拉莫斯国家实验室集成纳米技术中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于隐式分数匹配的粗粒化新框架，避免显式粗粒化力计算，减少训练数据，实现每个位点代表数百至数千原子，桥接分子与介观尺度，保持化学准确性。
AI 中文摘要
分子动力学模拟为原子尺度过程提供了计算显微镜，但仍局限于相对较小的空间和时间尺度。粗粒化模型通过将原子组表示为有效相互作用位点来扩展其范围。通过将原子组的集体行为嵌入到由有效势控制的粗粒化位点中，粒子数量被系统地减少。然而，即使借助现代机器学习方法，构建准确的高度粗粒化势能仍是一个重大挑战，因为粗粒化力依赖于复合原子构型的统计分布，使得直接计算在计算上不可行。在此，我们介绍了一种基于隐式分数匹配的替代框架，直接避免了对显式粗粒化力的需求。该方法相对于传统的力匹配方法大幅减少了所需的训练数据，并能够实现每个粗粒化位点代表数百至数千个原子的高保真模型。我们展示了该方法在多种化学体系和现象中的通用性和有效性，开发了自下而上、机器学习的模型。这些模型能够在微米和毫米长度尺度上进行高效建模，同时保留大量的原子尺度保真度。这些结果确立了隐式分数匹配作为实现桥接分子与介观尺度的化学准确模拟的实用途径。
英文摘要
Molecular dynamics simulations provide a computational microscope for atomic scale processes but remain restricted to relatively small spatial and temporal scales. Coarse-grained models extend their reach by representing groups of atoms as effective interaction sites. The number of particles is systematically reduced by embedding the collective behavior of groups of atoms into coarse-grained sites governed by effective potentials. However, constructing accurate, highly coarsened potentials, even with the assistance of modern machine-learning methods, is a grand challenge because coarse grained forces depend on a statistical distribution of composite atomic configurations making direct calculation computationally prohibitive. Here, we introduce an alternative framework based on implicit score matching that directly avoids the need for explicit coarse-grained forces. This approach both substantially reduces the training data required relative to conventional force-matching methods and enables high-fidelity models in which each coarse-grained site represents hundreds to thousands of atoms. We demonstrate the method's versatility and effectiveness across a diverse range of chemical systems and phenomena, developing bottom-up, machine-learned models. These enable efficient modeling at micrometer and millimeter length scales, while retaining significant amounts of the atomic scale fidelity. These results establish implicit score matching as a practical route towards chemically accurate simulations that bridge molecular and mesoscopic scales.
