Research data supporting "Designing a machine learning potential for molecular simulation of liquid alkanes"
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Veit, M. (2019). Research data supporting "Designing a machine learning potential for molecular simulation of liquid alkanes" [Dataset]. https://doi.org/10.17863/CAM.39133
Data supporting the PhD thesis: Trajectories, samples, potentials, and simulation parameters. Detailed contents given in the README files within.
QUIP (https://github.com/libAtoms/QUIP), LAMMPS (http://lammps.sandia.gov), and i-PI (http://ipi-code.org) are required. ASE(https://wiki.fysik.dtu.dk/ase/) may also be used to read atomic structures and properties. See README files for usage instructions.
machine learning, molecular simulation, density functional theory, many-body interactions, dispersion, alkanes
Publication Reference: https://doi.org/10.17863/CAM.37522
First-year training funded by the EPSRC as part of the centre for doctoral training in computational methods for materials science (CDT CMM) under grant number EP/L015552/1. PhD studentship funding by Shell Global Solutions International B.V. Computer time provided by ARCHER (http://archer.ac.uk) under the UKCP Consortium, EPSRC grant number EP/P022596/1.
This record's DOI: https://doi.org/10.17863/CAM.39133