Bug#790803: ITP: neural -- machine-learning for atomistics

[Muammar El Khatib]

Hi,

On Thu, Jul 16, 2015 at 7:53 AM, Andreas Tille  wrote:
>
> I think this ITP is relevant for Debian Science and DebiChem.  I guess
> you intend to maintain it in one of these teams.  Please make sure it
> will be added to the according Blends tasks (I'm fine if you tell me to
> what task what binary package should be added).
>
> It would be nice if you would CC the relevant teams in the beginning
> (and sorry if I missed the announcement).
>
> Thanks for this ITP.

I have a Debian package done for Amp¹ that I built inspired by
Graham's previous packaging that I plan to upload after this latest
Debian release.  I think Debian Science is a good fit for it. I am
very interested in this program because I am currently doing my
postdoc in the group where it is developed and I am working on it a
lot. I will start by moving the repo to /git/debian-science/packages/.

Best,

1. https://anonscm.debian.org/git/collab-maint/amp.git/
-- 
Muammar El Khatib.
http://muammar.me

Bug#790803: ITP: neural -- machine-learning for atomistics

[Andreas Tille]

Hi Graham,

I think this ITP is relevant for Debian Science and DebiChem.  I guess
you intend to maintain it in one of these teams.  Please make sure it
will be added to the according Blends tasks (I'm fine if you tell me to
what task what binary package should be added).

It would be nice if you would CC the relevant teams in the beginning
(and sorry if I missed the announcement).

Thanks for this ITP

 Andreas.

On Wed, Jul 01, 2015 at 10:51:15PM +0200, Graham Inggs wrote:
 Package: wnpp
 Severity: wishlist
 Owner: Graham Inggs gra...@nerve.org.za
 X-Debbugs-CC: debian-de...@lists.debian.org
 
 * Package name: neural
   Version : 1.0
   Upstream Author : Andrew Peterson, Alireza Khorshidi
 * URL : https://bitbucket.org/andrewpeterson/neural
 * License : GPL-3.0+
   Programming Lang: Python
   Description : Machine Learning for Atomistics
  Neural is an open-source code designed to easily bring machine-learning to
  atomistic calculations. This allows one to predict (or really, interpolate)
  calculations on the potential energy surface, by optimizing a neural network
  representation of a training set of atomic images. The code works by
  learning from any other calculator (usually DFT) that can provide energy as
  a function of atomic coordinates. In theory, these predictions can take place
  with arbitrary accuracy approaching that of the original calculator.
  .
  Neural is designed to integrate closely with the Atomic Simulation
  Environment (ASE). As such, the interface is in pure python, although several
  compute-heavy parts of the underlying code also have fortran versions to
  accelerate the calculations. The close integration with ASE means that any
  calculator that works with ASE ─ including EMT, GPAW, DACAPO, VASP, NWChem,
  and Gaussian ─ can easily be used as the parent method.
 
 
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Bug#790803: ITP: neural -- machine-learning for atomistics

[Graham Inggs]

Package: wnpp
Severity: wishlist
Owner: Graham Inggs gra...@nerve.org.za
X-Debbugs-CC: debian-de...@lists.debian.org

* Package name: neural
  Version : 1.0
  Upstream Author : Andrew Peterson, Alireza Khorshidi
* URL : https://bitbucket.org/andrewpeterson/neural
* License : GPL-3.0+
  Programming Lang: Python
  Description : Machine Learning for Atomistics
 Neural is an open-source code designed to easily bring machine-learning to
 atomistic calculations. This allows one to predict (or really, interpolate)
 calculations on the potential energy surface, by optimizing a neural network
 representation of a training set of atomic images. The code works by
 learning from any other calculator (usually DFT) that can provide energy as
 a function of atomic coordinates. In theory, these predictions can take place
 with arbitrary accuracy approaching that of the original calculator.
 .
 Neural is designed to integrate closely with the Atomic Simulation
 Environment (ASE). As such, the interface is in pure python, although several
 compute-heavy parts of the underlying code also have fortran versions to
 accelerate the calculations. The close integration with ASE means that any
 calculator that works with ASE ─ including EMT, GPAW, DACAPO, VASP, NWChem,
 and Gaussian ─ can easily be used as the parent method.


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