Computational workflows that combine molecular dynamics (MD) simulations and emerging data-centric (DC) methods can accelerate the screening and analysis of solvent systems experimentally and computationally. MD simulations provide atomic positions and velocities of reactant, solvent, and catalyst materials that can be manipulated into data representations that in turn can be used by DC techniques to conduct predictive modeling, feature extraction, and experimental design. For liquid-phase catalytic applications, emerging DC techniques such as Convolutional and Graph Neural Networks (CNN/GNN), Topological Data Analysis (TDA), and Active Learning (AL) can leverage MD and experimental data to quickly predict solvent effects on reaction outcomes. For instance, in recent studies, 3D solvent environments obtained with MD have been exploited by CNNs to predict experimental reaction rates for homogeneous acid-catalyzed lignocellulosic processes. In this perspective, we discuss basic principles of DC methods and how these can be combined with MD to enable high-throughput screening of solvent selection for diverse catalysis applications.

结合分子动力学 (MD) 模拟和新兴以数据为中心 (DC) 方法的计算工作流程可以通过实验和计算加速溶剂系统的筛选和分析。MD 模拟提供反应物、溶剂和催化剂材料的原子位置和速度，可以将其处理成数据表示，而这些数据表示又可以被 DC 技术用于进行预测建模、特征提取和实验设计。对于液相催化应用，卷积和图神经网络 (CNN/GNN)、拓扑数据分析 (TDA) 和主动学习 (AL) 等新兴 DC 技术可以利用 MD 和实验数据快速预测溶剂对反应结果的影响. 例如，在最近的研究中，CNN 已利用 MD 获得的 3D 溶剂环境来预测均相酸催化木质纤维素过程的实验反应速率。从这个角度来看，我们讨论了 DC 方法的基本原理，以及如何将这些原理与 MD 相结合，以实现针对不同催化应用的溶剂选择的高通量筛选。