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

Package: wnpp
Severity: wishlist
Owner: Graham Inggs <graham@nerve.org.za>
X-Debbugs-CC: debian-devel@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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