Atomic simulations based on quantum mechanics (QM) calculations have entered into the tool box of chemists over the past few decades, facilitating an understanding of a wide range of chemistry problems, from structure characterization to reactivity determination. Due to the poor scaling and high computational cost intrinsic to QM calculations, one has to either sacrifice accuracy or time when performing large-scale atomic simulations. The battle to find a better compromise between accuracy and speed has been central to the development of new theoretical methods.

中文翻译：

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通过由全球势能面探索构建的机器学习势能进行大规模原子模拟。

在过去的几十年中，基于量子力学（QM）计算的原子模拟已进入化学家的工具箱，从而有助于人们理解从结构表征到反应性确定等一系列广泛的化学问题。由于质量管理计算固有的缩放比例差和计算成本高的问题，执行大规模原子模拟时必须牺牲精度或时间。在精度和速度之间寻求更好的折衷的斗争一直是新理论方法发展的核心。