Classical DFT offers an incredibly versatile and user-friendly framework for modelling many-body systems. Yet, its potential for widespread adoption across a variety of fields, including biology, nanofluidics, chemical engineering -- to name but a few -- is held back by the simple fact that accurate free energy functionals are known only for a handful of rather special systems. Present work is, to our knowledge, a first attempt to develop an algorithmic data-driven inference method for classical DFT functionals, equipped with full uncertainty quantification. Present work offers a first step towards inferential modelling of many-body systems, where small-scale simulations are used to algorithmically capture essential patterns of their collective behaviour. Thus, yielding an analytic description that can be scaled to system sizes beyond simulation capabilities.
翻译：古典DFT为模拟多机体系统提供了一个难以置信的多功能和方便用户的框架。然而,由于精确的免费能源功能只为少数相当特殊的系统所知道的简单事实,它有可能被广泛应用于多个领域,包括生物学、纳米氟化物、化学工程 -- -- 仅举几个例子 -- -- 被阻碍。 据我们所知,目前的工作是首次尝试为古典DFT功能开发一种算法数据驱动推理法方法,配有完全的不确定性量化。目前的工作是朝着多机体系统的推断建模迈出的第一步,在这些系统中,小规模的模拟用于从逻辑上捕捉到其集体行为的基本模式。因此,产生了一个分析性描述,可以扩大到超出模拟能力的系统规模。
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