计算机模拟中的水溶液化学及数据驱动方法的作用
Aqueous Solution Chemistry In Silico and the Role of Data Driven Approaches
AI总结：
本文梳理了水溶液化学模拟中多体与反应势能开发的进展，分析了相关概念和方法挑战，并展望了数据科学技术对物理化学研究及水相体系模拟的未来影响。
摘要：
如今，利用计算机模拟研究水相体系的性质比以往任何时候都更受关注，是一个活跃的研究领域。在此背景下，过去十年间，数据驱动方法的应用大幅增长，既用于开发更精确的水的势能模型，也用于表征其在化学和生物环境中的复杂性。本文结合历史背景，梳理了在开发用于模拟水溶液化学的多体势能和反应势能路径上的进展，其中包括机器学习策略的作用。我们重点聚焦于开展旨在解决水溶液化学建模问题的模拟过程中，在概念和方法层面遇到的挑战。最后，我们总结了对应用和整合先进数据科学技术以提供物理化学领域化学洞见的展望，以及这将如何影响未来水相体系的计算机模拟。
英文摘要：
The use of computer simulations to study the properties of aqueous systems is, today more than ever, an active area of research. In this context, during the last decade there has been a tremendous growth in the use of data-driven approaches to develop more accurate potentials for water as well as to characterize its complexity in chemical and biological contexts. We highlight the progress, giving a historical context, on the path to the development of many-body and reactive potentials to model aqueous chemistry, including the role of machine learning strategies. We focus specifically on conceptual and methodological challenges along the way in performing simulations that seek to tackle problems in modeling the chemistry of aqueous solutions. In conclusion, we summarize our perspectives on the use and integration of advanced data-science techniques to provide chemical insights in physical chemistry and how this will influence computer simulations of aqueous systems in the future.
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。
