Data-Driven Approach to Coarse-Graining Simple Liquids in Confinement
Data-Driven Approach to Coarse-Graining Simple Liquids in Confinement
复制标题
限制中粗粒简单液体的数据驱动方法
DOI：
10.1021/acs.jctc.3c00633
复制
发表时间：
2023
影响因子：
5.5
通讯作者：
Aluru, Narayana R.
中科院分区：
文献类型：
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作者：
Nadkarni, Ishan;Wu, Haiyi;Aluru, Narayana R.
我们提出了一个数据驱动的框架，用于识别简单液体受限系统中的粗粒度（CG） Lennard-Jones （LJ）势参数。我们的方法涉及使用深度神经网络（DNN），该网络被训练来近似求解受限系统的反液相（ILST）问题。DNN模型固有地包含了特定于受限流体的基本物理特征，从而能够准确预测非均匀性效应。通过迁移学习，我们预测了被限制在狭缝状通道中的简单多原子液体的单点LJ势，当静电相互作用不占主导地位时，该通道有效地复制了目标全原子（AA）体系的流体结构和分子力。此外，我们展示了数据驱动方法与利用相对熵（RE）最小化的众所周知的自下而上粗粒度方法之间的协同作用。通过这两种方法的顺序使用，迭代正则方法的鲁棒性得到了显著增强，收敛性得到了显著提高。
We propose a data-driven framework for identifying coarse-grained (CG) Lennard-Jones (LJ) potential parameters in confined systems for simple liquids. Our approach involves the use of a Deep Neural Network (DNN) that is trained to approximate the solution of the Inverse Liquid State (ILST) problem for confined systems. The DNN model inherently incorporates essential physical characteristics specific to confined fluids, enabling an accurate prediction of inhomogeneity effects. By utilizing transfer learning, we predict single-site LJ potentials of simple multiatomic liquids confined in a slit-like channel, which effectively replicate both the fluid structure and molecular force of the target All-Atom (AA) system when the electrostatic interactions are not dominant. In addition, we showcase the synergy between the data-driven approach and the well-known Bottom-Up coarse-graining method utilizing Relative-Entropy (RE) Minimization. Through the sequential utilization of these two methods, the robustness of the iterative RE method is significantly augmented, leading to a remarkable enhancement in convergence.