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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.
These GROMACS trajectories show the existence of a critical point in deeply supercooled WAIL water. Also included is the code necessary to reproduce the figures in the corresponding paper from these trajectories. From this data the critical temperature, pressure, and density of the model can be found, and critical fluctuations in the deeply supercooled liquid can be directly observed (in a computer-simulation sense).
This distribution compiles numerous physical properties for 2,585 intrinsically disordered proteins (IDPs) obtained by coarse-grained molecular dynamics simulation. This combination comprises "Dataset A" as reported in "Featurization strategies for polymer sequence or composition design by machine learning" by Roshan A. Patel, Carlos H. Borca, and Michael A. Webb (DOI: 10.1039/D1ME00160D). The specific IDP sequences are sourced from version 9.0 of the DisProt database. The simulations were performed using the LAMMPS molecular dynamics engine. The interactions used for simulation are obtained from R. M. Regy , J. Thompson , Y. C. Kim and J. Mittal , Improved coarse-grained model for studying sequence dependent phase separation of disordered proteins, Protein Sci., 2021, 1371 —1379.
There has been considerable recent interest in the high-pressure behavior of silicon carbide, a potential major constituent of carbon-rich exoplanets. In this work, the atomic-level structure of SiC was determined through in situ X-ray diffraction under laser-driven ramp compression up to 1.5 TPa; stresses more than seven times greater than previous static and shock data. Here we show that the B1-type structure persists over this stress range and we have constrained its equation of state (EOS). Using this data we have determined the first experimentally based mass-radius curves for a hypothetical pure SiC planet. Interior structure models are constructed for planets consisting of a SiC-rich mantle and iron-rich core. Carbide planets are found to be ~10% less dense than corresponding terrestrial planets.
This dataset includes individual CIF files with the refined structure of fluorapatite under compression to 61 GPa. The structures have been discussed in detail in the accompanying manuscript "Single-crystal X-ray diffraction of fluorapatite to 61 GPa"
The dataset contains the model file for the Global Adjoint Tomography Model 25 (GLAD-M25). The model file contains parameters defined on the spectral-element mesh and is recommend to be used in SPECFEM3D GLOBE for seismic wave simulation at the global scale.
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.
Schwartz, Jacob A.; Nelson, A. O.; Kolemen, Egemen
Abstract:
Shaping a tokamak plasma to have a negative triangularity may allow operation in an ELM-free L-mode regime and with a larger strike-point radius, ameliorating divertor power-handling requirements. However, the shaping has a potential drawback in the form of a lower no-wall ideal beta limit, found using the MHD codes CHEASE and DCON. Using the new fusion systems code FAROES, we construct a steady-state DEMO2 reactor model. This model is essentially zero-dimensional and neglects variations in physical mechanisms like turbulence, confinement, and radiative power limits, which could have a substantial impact on the conclusions deduced herein. Keeping its shape otherwise constant, we alter the triangularity and compute the effects on the levelized cost of energy (LCOE). If the tokamak is limited to a fixed B field, then unless other means to increase performance (such as reduced turbulence, improved current drive efficiency or higher density operation) can be leveraged, a negative-triangularity reactor is strongly disfavored in the model due to lower \beta_N limits at negative triangularity, which leads to tripling of the LCOE. However, if the reactor is constrained by divertor heat fluxes and not by magnet engineering, then a negative-triangularity reactor with higher B0 could be favorable: we find a class of solutions at negative triangularity with lower peak heat flux and lower LCOE than those of the equivalent positive triangularity reactors.
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.
