[CP2K-user] [CP2K:15594] Re: Machine Learning Force Fields

Mostafa Abedi abedimo... at gmail.com
Fri Jun 18 13:03:17 UTC 2021

 Dear Ana,
I have been working in the fields of machine learning potentials (force
fields) for a couple of years. I would be happy to share some of
experiences with you. You can contact me via "mostaf... at brown.edu", if
you like.


On Fri, Jun 18, 2021 at 8:59 AM Nicklas Österbacka <
nicklas.... at gmail.com> wrote:

> n2p2 <https://compphysvienna.github.io/n2p2> implements
> Behler-Parinello-style neural network potentials. There is also a plugin
> for the MD package QUIP that implements Bartók's kernel-based Gaussian
> Approximation Potential <https://libatoms.github.io/GAP/>.
> There are plenty more, but those two should give you something to test
> things out with and are code-agnostic. You do have to prepare the data set
> for training, however. Writing a script to do so should not be particularly
> difficult.
> Good luck,
> Nicklas
> fredag 18 juni 2021 kl. 14:33:38 UTC+2 skrev aw... at gmail.com:
>> Dear All,
>> Is there any straighforward way of generating machine learning force
>> fields using a CP2K trajectory? I noticed that most of the tools
>> available online use VASP or QUANTUM ESPRESSO. I am looking for a tool
>> which allows me to construct a force field by training on both energy and
>> forces, but the tool needs to be useful for a complete newbie. Any ideas
>> ?
>> Best wishes,
>> Ana
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