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Gartner, Thomas III; Zhang, Linfeng; Piaggi, Pablo; Car, Roberto; Panagiotopoulos, Athanassios; Debenedetti, Pablo
Abstract:
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.
We provide all the test data and corresponding predictions for our paper, “Practical Fluorescence Reconstruction Microscopy for High-Content Imaging”. Please refer to the Methods section in this paper for experimental details. For each experimental condition, we provide the input transmitted-light images (either phase contrast or DIC), the ground truth fluorescence images, and the output predicted fluorescence images which should reconstruct the ground truth fluorescence images.
This dataset contains input and output files to reproduce the results of the manuscript "Homogeneous ice nucleation in an ab initio machine learning model" by Pablo M. Piaggi, Jack Weis, Athanassios Z. Panagiotopoulos, Pablo G. Debenedetti, and Roberto Car (arXiv preprint https://arxiv.org/abs/2203.01376). In this work, we studied the homogeneous nucleation of ice from supercooled liquid water using a machine learning model trained on ab initio energies and forces. Since nucleation takes place over times much longer than the simulation times that can be afforded using molecular dynamics simulations, we make use of the seeding technique that is based on simulating an ice cluster embedded in liquid water. The key quantity provided by the seeding technique is the size of the critical cluster (i.e., a size such that the cluster has equal probabilities of growing or shrinking at the given supersaturation). Using data from the seeding simulations and the equations of classical nucleation theory we compute nucleation rates that can be compared with experiments.
