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121. Data from "Homogeneous ice nucleation in an ab initio machine learning model"

Author(s):
Piaggi, Pablo
Abstract:
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.
Type:
Dataset
Issue Date:
4 April 2022

122. Data from "Liquid-liquid transition in water from first principles"

Author(s):
Gartner, Thomas
Abstract:
This dataset contains all data relevant to a forthcoming publication in which we used molecular simulation methods to study the phase behavior of supercooled water. The dataset contains simulation input and output files, processed data files, and image files used to create all plots in the manuscript. Python analysis scripts are also included, including instructions for how to re-generate all plots in the manuscript.
Type:
Dataset
Issue Date:
26 July 2022

124. Data from "Melting curves of ice polymorphs in the vicinity of the liquid-liquid critical point"

Author(s):
Piaggi, Pablo M; Gartner, Thomas E; Car, Roberto; Debenedetti, Pablo G
Abstract:
The possible existence of a liquid-liquid critical point in deeply supercooled water has been a subject of debate in part due to the challenges associated with providing definitive experimental evidence. Pioneering work by Mishima and Stanley [Nature 392, 164 (1998) and Phys.~Rev.~Lett. 85, 334 (2000)] sought to shed light on this problem by studying the melting curves of different ice polymorphs and their metastable continuation in the vicinity of the expected location of the liquid-liquid transition and its associated critical point. Based on the continuous or discontinuous changes in slope of the melting curves, Mishima suggested that the liquid-liquid critical point lies between the melting curves of ice III and ice V. Here, we explore this conjecture using molecular dynamics simulations with a purely-predictive machine learning model based on ab initio quantum-mechanical calculations. We study the melting curves of ices III, IV, V, VI, and XIII using this model and find that the melting lines of all the studied ice polymorphs are supercritical and do not intersect the liquid-liquid transition locus. We also find a pronounced, yet continuous, change in slope of the melting lines upon crossing of the locus of maximum compressibility of the liquid. Finally, we analyze critically the literature in light of our findings, and conclude that the scenario in which melting curves are supercritical is favored by the most recent computational and experimental evidence. Thus, although the preponderance of experimental and computational evidence is consistent with the existence of a second critical point in water, the behavior of the melting lines of ice polymorphs does not provide strong evidence in support of this viewpoint, according to our calculations.
Type:
Dataset
Issue Date:
February 2023

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

Author(s):
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.
Type:
Dataset
Issue Date:
July 2020

128. Data from "Transferability of data-driven, many-body models for CO2 simulations in the vapor and liquid phases"

Author(s):
Yue, Shuwen; Riera, Marc
Abstract:
This dataset comprises of data associated with the publication "Transferability of data-driven, many-body models for CO2 simulations in the vapor and liquid phases", which can be found at https://doi.org/10.1063/5.0080061. The data includes calculations for a Many-Body decomposition, virial coefficient calculations, orientational molecular scan energies, potential energy fields, correlation plots of training and testing data, vapor-liquid equilibrium simulations, liquid density simulations, and solid cell simulations.
Type:
Dataset
Issue Date:
30 March 2022

129. Data from "Vapor-liquid equilibrium of water with the MB-pol many-body potential"

Author(s):
Muniz, Maria Carolina; Gartner III, Thomas E.; Riera, Marc; Knight, Christopher; Yue, Shuwen; Paesani, Francesco; Panagiotopoulos, Athanassios Z.
Abstract:
This dataset contains all data (including input files, simulation trajectories as well as other data files and analysis scripts) related to the publication "Vapor-liquid equilibrium of water with the MB-pol many-body potential" by Muniz et al. in preparation (2021). In this work, we assessed the performance of the MB-pol many-body potential with respect to water's vapor-liquid equilibrium properties. Through the use of direct coexistence molecular dynamics, we calculated properties such as coexistence densities, surface tension, vapor pressures and enthalpy of vaporization. We found that MB-pol is able to predict these properties in good agreement with experimental data. The results attest to the chemical accuracy of MB-pol and its large range of application across water's phase diagram.
Type:
Dataset
Issue Date:
2021

130. Data from a flume experiment of passive scalar diffusion within vegetation canopies using laser-induced fluorescence

Author(s):
Ghannam, Khaled; Poggi, Davide; Katul, Gabriel; Bou-Zeid, Elie
Abstract:
This dataset is a sequence of laser-induced fluorescence images of a dye injected in a channel flow with canopy-like stainless steel rods simulating a vegetation canopy stand. The data is acquired close to the channel bottom at z/h=0.2, where z is the height referenced to the channel bed and h is the canopy height. The dataset provides spatial distribution of scalar concentration in a plane parallel to the channel bed. The data has been used (but the data itself has not been published or available to the public) in previous work. The references are: Ghannam, K., Poggi, D., Porporato, A., & Katul, G. (2015). The spatio-temporal statistical structure and ergodic behaviour of scalar turbulence within a rod canopy. Boundary-Layer Meteorology,157(3), 447–460. Ghannam, K, Poggi, D., Bou-Zeid, E., Katul, G. (2020). Inverse cascade evidenced by information entropy of passive scalars in submerged canopy flows. Geophysical Research Letters (accepted).
Type:
Dataset
Issue Date:
22 April 2020

135. Data set for "NAPS: Integrating pose estimation and tag-based tracking"

Author(s):
Wolf, Scott W.
Abstract:
Data set corresponding to "NAPS: Integrating pose estimation and tag-based tracking." This dataset contains the corresponding videos, tracking scripts, and SLEAP models along with SLEAP, NAPS, and ArUco tracking results.
Type:
Dataset
Issue Date:
7 December 2022