Google has released an open-source version of its machine-learning software named Tensorflow, which can allow for efficient backpropagation of neural networks and utilization of GPUs for extra speed.

We have incorporated an experimental module that uses a tensorflow back-end, which may provide an acceleration particularly through access to GPU systems. As of this writing, the tensorflow code is in flux (with version 1.0 anticipated shortly).


This package requires google’s TensorFlow 0.11.0. You can install it as shown below for Linux:

export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.11.0-cp27-none-linux_x86_64.whl
pip install -U --upgrade $TF_BINARY_URL

or macOS:

export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.11.0-py2-none-any.whl
pip install -U --upgrade $TF_BINARY_URL

If you want more information, please see tensorflow’s website for instructions for installation on your system.


#!/usr/bin/env python
"""Simple test of the Amp calculator, using Gaussian descriptors and neural
network model. Randomly generates data with the EMT potential in MD

from ase.calculators.emt import EMT
from ase.build import fcc110
from ase import Atoms, Atom
from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
from ase import units
from ase.md import VelocityVerlet
from ase.constraints import FixAtoms

from amp import Amp
from amp.descriptor.gaussian import Gaussian
from amp.model.tflow import NeuralNetwork

def generate_data(count):
    """Generates test or training data with a simple MD simulation."""
    atoms = fcc110('Pt', (2, 2, 2), vacuum=7.)
    adsorbate = Atoms([Atom('Cu', atoms[7].position + (0., 0., 2.5)),
                       Atom('Cu', atoms[7].position + (0., 0., 5.))])
    atoms.set_constraint(FixAtoms(indices=[0, 2]))
    atoms.calc = EMT()
    MaxwellBoltzmannDistribution(atoms, 300. * units.kB)
    dyn = VelocityVerlet(atoms, dt=1. * units.fs)
    newatoms = atoms.copy()
    newatoms.calc = EMT()
    images = [newatoms]
    for step in range(count - 1):
        newatoms = atoms.copy()
        newatoms.calc = EMT()
    return images

def train_test():
    label = 'train_test/calc'
    train_images = generate_data(2)
    convergence = {
            'energy_rmse': 0.02,
            'force_rmse': 0.02

    calc = Amp(descriptor=Gaussian(),
               model=NeuralNetwork(hiddenlayers=(3, 3), convergenceCriteria=convergence),

    for image in train_images:
        print "energy =", calc.get_potential_energy(image)
        print "forces =", calc.get_forces(image)

if __name__ == '__main__':

Known issues

  • tflow module does not work for versions different from 0.11.0.


This module was contributed by Zachary Ulissi (Department of Chemical Engineering, Stanford University, zulissi@gmail.com) with help, testing, and discussions from Andrew Doyle (Stanford) and the Amp development team.