Data from Signatures of a liquid-liquid transition in an ab-inito neural network model for water

This dataset contains all data related to the publication "Signatures of a liquid-liquid transition in an ab-inito neural network model for water", by Gartner et al., 2020. In this work, we used neural networks to generate a computational model for water using high-accuracy quantum chemistry calculations. Then, we used advanced molecular simulations to demonstrate evidence that suggests this model exhibits a liquid-liquid transition, a phenomenon that can explain many of water's anomalous properties. This dataset contains links to all software used, all data generated as part of this work, as well as scripts to generate and analyze all data and generate the plots reported in the publication.

本数据集涵盖Gartner等人2020年发表的论文《水中从头算（ab-initio）神经网络模型的液液相变特征》相关的全部数据。该研究中，研究者借助高精度量子化学计算，通过神经网络构建了水的计算模型。随后，利用先进的分子模拟手段，研究者获取了该模型存在液液相变（liquid-liquid transition）的相关证据——这一现象可解释水的诸多反常物理性质。本数据集包含本研究中使用的全部软件的链接、研究生成的所有数据，以及用于生成、分析所有数据并绘制论文中所载图表的脚本文件。