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31. 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

32. 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

33. 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

34. Data on Enzyme Activity Retention in glucose oxidase, lipase, and horseradish peroxidase

Author(s):
Webb, Michael; Patel, Roshan; Gormley, Adam; Tamasi, Matt; Borca, Carlos; Kosuri, Shashank
Abstract:
This distribution contains experimentally measured data for the extent of retained enzyme activity post thermal stressing for three distinct enzymes: glucose oxidase, lipase, and horseradish peroxidase. The data is used to form conclusions and develop machine learning models as reported in the publication "Machine Learning on a Robotic Platform for the Design of Polymer-Protein Hybrids" by Matthew Tamasi, Roshan Patel, Carlos Borca, Shashank Kosuri, Heloise Mugnier, Rahul Upadhya, N. Sanjeeva Murthy, Michael Webb*, and Adam Gormley. Details regarding the experimental protocols are reported in the aforementioned paper but are briefly discussed in the README.
Type:
Dataset
Issue Date:
17 May 2022

35. 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

36. Data set for "Ocean emission of microplastic by bursting bubble jet drops"

Author(s):
Shaw, Daniel B.; Deike, Luc
Abstract:
Data set for "Ocean emission of microplastic by bursting bubble jet drops." Two .csv files are provided: one for the size of a jet drop carrying microplastic, and another for the amount of microplastic captured by a jet drop.
Type:
Dataset
Issue Date:
21 December 2022

39. Effects of collisional ion orbit loss on neoclassical tokamak radial electric fields

Author(s):
Zhu, Hongxuan; Stoltzfus-Dueck, T; Hager, R; Ku, S; Chang, C. S.
Abstract:
Ion orbit loss is considered important for generating the radially inward electric field Er in a tokamak edge plasma. In particular, this effect is emphasized in diverted tokamaks with a magnetic X point. In neoclassical equilibria, Coulomb collisions can scatter ions onto loss orbits and generate a radially outward current, which in steady state is balanced by the radially inward current from viscosity. To quantitatively measure this loss-orbit current in an edge pedestal, an ion-orbit-flux diagnostic has been implemented in the axisymmetric version of the gyrokinetic particle-in-cell code XGC. As the first application of this diagnostic, a neoclassical DIII-D H-mode plasma is studied using gyrokinetic ions and adiabatic electrons. The validity of the diagnostic is demonstrated by studying the collisional relaxation of Er in the core. After this demonstration, the loss-orbit current is numerically measured in the edge pedestal in quasisteady state. In this plasma, it is found that the radial electric force on ions from Er approximately balances the ion radial pressure gradient in the edge pedestal, with the radial force from the plasma flow term being a minor component. The effect of orbit loss on Er is found to be only mild.
Type:
Dataset
Issue Date:
2022

40. Electron Temperature Gradient Driven Transport Model for Tokamak Plasmas

Author(s):
Rafiq, Tariq; Wilson, Christopher; Luo, Lixiang; Weiland, Jan; Schuster, Eugenio; Pankin, Alexei; Guttenfelder, Walter; Kaye, Stan
Abstract:
A new model for electron temperature gradient (ETG) modes is developed as a component of the Multi-Mode anomalous transport module [T. Rafiq \textit{et al.,} Phys Plasmas \textbf{20}, 032506 (2013)] to predict a time dependent electron temperature profile in conventional and low aspect ratio tokamaks. This model is based on two-fluid equations that govern the dynamics of low-frequency short- and long-wavelength electromagnetic toroidal ETG driven drift modes. A low collisionality NSTX discharge is used to scan the plasma parameter dependence on the ETG real frequency, growth rate, and electron thermal diffusivity. Electron thermal transport is discovered in the deep core region where modes are more electromagnetic in nature. Several previously reported gyrokinetic trends are reproduced, including the dependencies of density gradients, magnetic shear, $\beta$ and gradient of $\beta$ $(\betap)$, collisionality, safety factor, and toroidicity, where $\beta$ is the ratio of plasma pressure to the magnetic pressure. The electron heat diffusivity associated with the ETG mode is discovered to be on a scale consistent with the experimental diffusivity determined by power balance analysis.
Type:
Dataset
Issue Date:
30 August 2022