Cascella Michele, Bore Sigbjørn Løland, Eisenstein Odile
Department of Chemistry and Hylleraas Centre for Quantum Molecular Sciences, University of Oslo PO Box 1033 Blindern 0315 Oslo Norway
ICGM, Univ. Montpellier, CNRS, ENSCM Montpellier 34293 France
Chem Sci. 2025 Apr 11;16(19):8196-8216. doi: 10.1039/d5sc01078k. eCollection 2025 May 14.
This perspective begins with the discovery of the Grignard reaction by a graduate student in the last years of the 19th century, followed by describing why it has remained largely unexplained for more than a century. From the summary of what has been achieved, focusing on the computational aspects, it is now clear that further studies of the chemistry of any chemical species that is highly sensitive to solvents, such as Group I and II elements, require a holistic approach that includes the solute and the solvent together. molecular dynamics, which meets these requirements, has produced some results but has hit hard limits due to its relatively high computational costs. In these days, it is becoming clear that data-driven methods, including machine learning potentials and simulations driven by quantitative on-the-fly calculation of relevant observables, have the potential to better and more completely explore the very large chemical space associated with the presence of a large number of species in solution. These methodologies have the chance to give the keys to enter the challenging and still poorly explored world of chemical species whose behaviour and reactivity are strongly influenced by the solvent and the experimental conditions.
这一观点始于19世纪最后几年一名研究生发现格氏反应，接着描述了为何在一个多世纪里它在很大程度上一直未得到解释。从已取得成果的总结来看，聚焦于计算方面，现在很清楚的是，对任何对溶剂高度敏感的化学物种（如第I族和第II族元素）的化学性质进行进一步研究，需要一种将溶质和溶剂一起考虑的整体方法。满足这些要求的分子动力学已经产生了一些结果，但由于其相对较高的计算成本而遇到了很大的限制。如今，越来越明显的是，数据驱动的方法，包括机器学习势以及由相关可观测量的定量即时计算驱动的模拟，有潜力更好、更全面地探索与溶液中大量物种存在相关的非常大的化学空间。这些方法有机会提供进入具有挑战性且仍未得到充分探索的化学物种世界的钥匙，这些化学物种的行为和反应性受到溶剂和实验条件的强烈影响。
