Department of Organic Materials Science, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States.
Geochemistry Department, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States.
J Phys Chem B. 2021 Dec 2;125(47):12990-13002. doi: 10.1021/acs.jpcb.1c07092. Epub 2021 Nov 18.
The ability to predict transport properties of liquids quickly and accurately will greatly improve our understanding of fluid properties both in bulk and complex mixtures, as well as in confined environments. Such information could then be used in the design of materials and processes for applications ranging from energy production and storage to manufacturing processes. As a first step, we consider the use of machine learning (ML) methods to predict the diffusion properties of pure liquids. Recent results have shown that Artificial Neural Networks (ANNs) can effectively predict the diffusion of pure compounds based on the use of experimental properties as the model inputs. In the current study, a similar ANN approach is applied to modeling diffusion of pure liquids using fluid properties obtained exclusively from molecular simulations. A diverse set of 102 pure liquids is considered, ranging from small polar molecules (e.g., water) to large nonpolar molecules (e.g., octane). Self-diffusion coefficients were obtained from classical molecular dynamics (MD) simulations. Since nearly all the molecules are organic compounds, a general set of force field parameters for organic molecules was used. The MD methods are validated by comparing physical and thermodynamic properties with experiment. Computational input features for the ANN include physical properties obtained from the MD simulations as well as molecular properties from quantum calculations of individual molecules. Fluid properties describing the local liquid structure were obtained from center of mass radial distribution functions (COM-RDFs). Feature sensitivity analysis revealed that isothermal compressibility, heat of vaporization, and the thermal expansion coefficient were the most impactful properties used as input for the ANN model to predict the MD simulated self-diffusion coefficients. The MD-based ANN successfully predicts the MD self-diffusion coefficients with only a subset (2 to 3) of the available computationally determined input features required. A separate ANN model was developed using literature experimental self-diffusion coefficients as model targets. Although this second ML model was not as successful due to a limited number of data points, a good correlation is still observed between experimental and ML predicted self-diffusion coefficients.
快速准确地预测液体输运性质将极大地提高我们对液体性质的理解，无论是在本体还是复杂混合物中，还是在受限环境中。这些信息可用于设计材料和工艺，应用范围从能源生产和储存到制造工艺。作为第一步，我们考虑使用机器学习 (ML) 方法来预测纯液体的扩散性质。最近的结果表明，人工神经网络 (ANN) 可以根据实验性质作为模型输入有效地预测纯化合物的扩散。在当前的研究中，类似的 ANN 方法被应用于使用仅从分子模拟中获得的流体性质来模拟纯液体的扩散。考虑了 102 种不同的纯液体，范围从小极性分子（如水）到大非极性分子（如辛烷）。自扩散系数是从经典分子动力学 (MD) 模拟中获得的。由于几乎所有的分子都是有机化合物，因此使用了一套通用的有机分子力场参数。MD 方法通过将物理和热力学性质与实验进行比较来验证。ANN 的计算输入特征包括从 MD 模拟中获得的物理性质以及单个分子量子计算的分子性质。描述局部液体结构的流体性质是从质心径向分布函数 (COM-RDF) 中获得的。特征敏感性分析表明，等温压缩系数、汽化热和热膨胀系数是作为 ANN 模型输入预测 MD 模拟自扩散系数最具影响力的性质。基于 MD 的 ANN 仅使用所需的计算确定输入特征的子集（2 到 3）成功预测了 MD 自扩散系数。使用文献实验自扩散系数作为模型目标，开发了另一个 ANN 模型。尽管由于数据点有限，第二个 ML 模型不是很成功，但实验和 ML 预测的自扩散系数之间仍然存在很好的相关性。
