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Reduction of interlayer interaction in multilayer stacking graphene with carb...
We insert carbon nanotubes (CNT) as nanospacers to modulate the microstructure of multilayer stacking graphene. Nanospacers can increase interlayer distance and reduce... -
Conformational Investigations in Flexible Molecules using Orientational NMR C...
This dataset has no description
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Machine learning of twin/matrix interfaces from local stress field
Twinning is an important deformation mode in plastically deformed hexagonal close-packed materials. The extremely high twin growth rates at the nanoscale make atomistic... -
The optimal resolution level of a protein is an emergent property of its stru...
Molecular dynamics simulations provide a wealth of data whose in-depth analysis can be computationally demanding and, sometimes, even unnecessary. Dimensionality reduction... -
Developments and further applications of ephemeral data derived potentials
Machine-learned interatomic potentials are fast becoming an indispensable tool in computational materials science. One approach is the ephemeral data-derived potential (EDDP),... -
Solvation Structure and UV-Visible Absorption Spectra of the Nitrate Anion at...
Nitrate is a significant contaminant in Polar snow. Its photolysis in environmental sunlight generates reactive nitrogen, which impacts the oxidative capacity of the atmosphere,... -
Data of the publication: Where Lennard-Jones Potentials Fail: Iterative Optim...
Jupyter notebook file and simulation data and results -
Data of the publication: Application of the 2PT model to understanding entrop...
Jupyter notebook files, tabulated potentials, simulation files, and density of states for the publication. -
Data of the publication: Iterative integral equation methods for structural c...
Jupyter notebook files, tabulated potentials, and simulation files for the publication. -
Data of the publication: Stability, Speed, and Constraints for Structural Coa...
Jupyter notebook files, tabulated potentials, and simulation files for the publication. -
Data of the publication: DosCalc: a parallelized tool to compute degree of fr...
Jupyter notebook file and simulation data and density of states -
Data for the publication: Iterative integral equation methods for structural ...
Jupyter notebook files, tabulated potentials, and simulation files for the publication. -
Understanding the Behavior of Fully Non-Toxic Polypyrrole-Gelatin and Polypyr...
Smart and soft electroactive polymer actuators as building blocks for soft robotics have many beneficial properties that could make them useful in future biomimetic and... -
A transferable force field for gallium nitride crystal growth from the melt u...
Atomic-scale simulations of reactive processes have been stymied by two factors: the lack of a suitable semi-empirical force field on the one hand, and the impractically large... -
A transferable force field for gallium nitride crystal growth from the melt u...
Atomic-scale simulations of reactive processes have been stymied by two factors: the lack of a suitable semi-empirical force field on the one hand, and the impractically large... -
A transferable force field for gallium nitride crystal growth from the melt u...
Atomic-scale simulations of reactive processes have been stymied by two factors: the general lack of a suitable semi-empirical force field on the one hand, and the impractically... -
Graph theory-based structural analysis on density anomaly of silica glass
Understanding the structure of glassy materials represents a tremendous challenge for both experiments and computations. Despite decades of scientific research, for instance,... -
The role of water in host-guest interaction
One of the main applications of atomistic computer simulations is the calculation of ligand binding free energies. The accuracy of these calculations depends on the force field... -
Deep learning the slow modes for rare events sampling
The development of enhanced sampling methods has greatly extended the scope of atomistic simulations, allowing long-time phenomena to be studied with accessible computational... -
Learning local equivariant representations for large-scale atomistic dynamics
A simultaneously accurate and computationally efficient parametrization of the energy and atomic forces of molecules and materials is a long-standing goal in the natural...
