基于对偶图卷积神经网络的离子液体温度传递粗粒化
Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks
摘要：
计算机模拟可以提供机理洞察离子液体（IL）和预测实验未实现的离子组合的性质。然而，离子液体遭受从一个特别大的差距，在时间尺度的原子和合奏运动。因此，粗粒度模型被用来代替昂贵的原子模拟，允许模拟更长的时间尺度和更大的系统。然而，构建定义粗粒度系统的结构和动力学的平均力的多体势可能是复杂的和计算密集的。机器学习在降维和学习平均力潜力的关键耦合挑战方面表现出巨大的潜力。为了提高离子液体的粗粒度，我们提出了一个神经网络模型训练的全原子经典分子动力学模拟。平均力势表示为两个联合训练的神经网络原子间相互作用势，分别学习耦合的短程和多体长程分子相互作用。这些原子间作用势将温度作为一个显式的输入变量，以捕获平均力势的温度依赖性。该模型再现结构的数量与高保真度，优于温度无关的基线在捕捉动态，概括到看不见的温度，并招致低仿真成本。
英文摘要：
Computer simulations can provide mechanistic insight into ionic liquids (ILs) and predict the properties of experimentally unrealized ion combinations. However, ILs suffer from a particularly large disparity in the time scales of atomistic and ensemble motion. Coarse-grained models are therefore used in place of costly atomistic simulations, allowing simulation of longer time scales and larger systems. Nevertheless, constructing the many-body potential of mean force that defines the structure and dynamics of a coarse-grained system can be complicated and computationally intensive. Machine learning shows great promise for the key coupled challenges of dimensionality reduction and learning the potential of mean force. To improve the coarse-graining of ILs, we present a neural network model trained on all-atom classical molecular dynamics simulations. The potential of mean force is expressed as two jointly-trained neural network interatomic potentials that learn the coupled short-range and the many-body long range molecular interactions. These interatomic potentials treat temperature as an explicit input variable to capture the temperature dependence of the potential of mean force. The model reproduces structural quantities with high fidelity, outperforms the temperature-independent baseline at capturing dynamics, generalizes to unseen temperatures, and incurs low simulation cost.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
