In materials science, accurately computing properties like viscosity, melting point, and glass transition temperatures solely through physics-based models is challenging. Data-driven machine learning (ML) also poses challenges in constructing ML models, especially in the material science domain where data is limited. To address this, we integrate physics-informed descriptors from molecular dynamics (MD) simulations to enhance the accuracy and interpretability of ML models. Our current study focuses on accurately predicting viscosity in liquid systems using MD descriptors. In this work, we curated a comprehensive dataset of over 4000 small organic molecules’ viscosities from scientific literature, publications, and online databases. This dataset enabled us to develop quantitative structure–property relationships (QSPR) consisting of descriptor-based and graph neural network models to predict temperature-dependent viscosities for a wide range of viscosities. The QSPR models reveal that including MD descriptors improves the prediction of experimental viscosities, particularly at the small data set scale of fewer than a thousand data points. Furthermore, feature importance tools reveal that intermolecular interactions captured by MD descriptors are most important for viscosity predictions. Finally, the QSPR models can accurately capture the inverse relationship between viscosity and temperature for six battery-relevant solvents, some of which were not included in the original data set. Our research highlights the effectiveness of incorporating MD descriptors into QSPR models, which leads to improved accuracy for properties that are difficult to predict when using physics-based models alone or when limited data is available.

中文翻译：

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推进材料特性预测：使用基于物理的机器学习模型来预测粘度

在材料科学中，仅通过基于物理的模型准确计算粘度、熔点和玻璃化转变温度等特性具有挑战性。数据驱动的机器学习 (ML) 也给构建 ML 模型带来了挑战，尤其是在数据有限的材料科学领域。为了解决这个问题，我们集成了分子动力学 (MD) 模拟中的物理描述符，以提高 ML 模型的准确性和可解释性。我们当前的研究重点是使用 MD 描述符准确预测液体系统中的粘度。在这项工作中，我们从科学文献、出版物和在线数据库中收集了超过 4000 种小有机分子粘度的综合数据集。该数据集使我们能够开发由基于描述符和图神经网络模型组成的定量结构-性能关系 (QSPR)，以预测各种粘度的温度依赖性粘度。QSPR 模型表明，包含 MD 描述符可以改善实验粘度的预测，特别是在少于一千个数据点的小数据集规模下。此外，特征重要性工具表明，MD 描述符捕获的分子间相互作用对于粘度预测最为重要。最后，QSPR 模型可以准确捕获六种电池相关溶剂的粘度和温度之间的反比关系，其中一些溶剂未包含在原始数据集中。我们的研究强调了将 MD 描述符纳入 QSPR 模型的有效性，这可以提高单独使用基于物理的模型或可用数据有限时难以预测的属性的准确性。