标题： 如何机器学习预测纳米受限下的流体密度 显示英文标题

标题： How Machine Learning Predicts Fluid Densities under Nanoconfinement

摘要： 在纳米尺度限制下的流体与它们的体相 counterparts 差异很大。纳米限制流体的一个臭名昭著的特征是它们在限制方向上的密度分布不均匀，这在纳米孔中的许多流体结构和传输现象中起着关键作用。近半个世纪以来在预测这一现象（流体分层）方面的理论努力表明，其复杂性难以通过纯分析方法处理；因此，目前几乎所有方法都广泛使用分子模拟，并且缺乏可推广的预测能力。在本工作中，我们证明了基于机器学习的模型（特别是随机森林模型），在大量分子模拟数据集上训练后，可以作为进一步分子模拟的可靠替代品。我们展示了这种随机森林模型在广泛的温度和限制尺度范围内具有出色的插值能力，甚至具有适度的外推能力。这些结果为开发通用、成本低于“纯”分子模拟且足够预测性的纳米限制流体性质模型提供了有希望的途径。 显示英文摘要

摘要： Fluids under nanoscale confinement differ -- and often dramatically -- from their bulk counterparts. A notorious feature of nanoconfined fluids is their inhomogeneous density profile along the confining dimension, which plays a key role in many fluid structural and transport phenomena in nanopores. Nearly five decades of theoretical efforts on predicting this phenomenon (fluid layering) have shown that its complexity resists purely analytical treatments; as a consequence, nearly all current approaches make extensive use of molecular simulations, and tend not to have generalizable predictive capabilities. In this work, we demonstrate that machine-learning-based models (in particular, a random forest model), trained upon large molecular simulation data sets, can serve as reliable surrogates in lieu of further molecular simulation. We show that this random forest model has excellent interpolative capabilities over a wide range of temperatures and confining lengthscales, and even has modest extrapolative ability. These results provide a promising pathway forward for developing models of nanoconfined fluid properties that are generalizable, lower cost than ``pure" molecular simulation, and sufficiently predictive for fluids-in-nanopores practitioners.}