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

This dataset contains all data related to the publication "Signatures
of a liquid-liquid transition in an ab initio deep 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年发表的研究论文《水的从头算（ab initio）深度神经网络模型中的液-液转变特征》（Signatures of a liquid-liquid transition in an ab initio deep neural network model for water）的全部相关数据。在该项研究中，研究团队依托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，借助先进的分子模拟手段，该团队为该模型呈现液-液转变现象提供了佐证，而这一现象可解释水的诸多反常物性。本数据集包含本研究中所用全部软件的链接、生成的所有实验数据，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代码。