Using Amp

If you are familiar with ASE, the use of Amp should be intuitive. At its most basic, Amp behaves like any other ASE calculator, except that it has a key extra method, called train, which allows you to fit the calculator to a set of atomic images. This means you can use Amp as a substitute for an expensive calculator in any atomistic routine, such as molecular dynamics, global optimization, transition-state searches, normal-mode analyses, phonon analyses, etc.

Basic use

To use Amp, you need to specify a descriptor and a model. The below shows a basic example of training Amp with Gaussian descriptors and a NeuralNetwork model—the Behler-Parinello scheme.

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

calc = Amp(descriptor=Gaussian(), model=NeuralNetwork(),

After training is successful you can use your trained calculator just like any other ASE calculator (although you should be careful that you can only trust it within the trained regime). This will also result in the saving the calculator parameters to “<label>.amp”, which can be used to re-load the calculator in a future session:

calc = Amp.load('calc.amp')

The modular nature of Amp is meant such that you can easily mix-and-match different descriptor and model schemes. See the theory section for more details.

Adjusting convergence parameters

To control how tightly the energy and/or forces are converged, you can adjust the LossFunction. Just insert before the calc.train line some code like:

from amp.model import LossFunction

convergence = {'energy_rmse': 0.02, 'force_rmse': 0.04}
calc.model.lossfunction = LossFunction(convergence=convergence)

You can see the adjustable parameters and their default values in the dictionary default_parameters:

>>> LossFunction.default_parameters
{'convergence': {'energy_rmse': 0.001, 'force_rmse': None, 'energy_maxresid': None, 'force_maxresid': None}}

Note that you can also set a maximum residual of any energy or force prediction with the appropriate keywords above.

To change how the code manages the regression process, you can use the Regressor class. For example, to switch from the scipy’s fmin_bfgs optimizer (the default) to scipy’s basin hopping optimizer, try inserting the following lines before initializing training:

from amp.regression import Regressor
from scipy.optimize import basinhopping

regressor = Regressor(optimizer=basinhopping)
calc.model.regressor = regressor

Turning on/off force training

Most electronic structure codes also give forces (in addition to potential energy) for each image, and you can get a much more predictive fit if you include this information while training. However, this can create issues: training will tend to be slower than training energies only, convergence will be more difficult, and if there are inconsistencies in the training data (say if the calculator reports 0K-extrapolated energies rather than force-consistent ones, or if there are egg-box errors), you might not be able to train at all. For this reason, Amp defaults to energy-only training, but you can turn on force-training via the convergence dictionary as noted above. Note that there is a force_coefficient keyword also fed to the LossFunction class which can control the relative weighting of the energy and force RMSEs used in the path to convergence.

from amp.model import LossFunction

convergence = {'energy_rmse': 0.02, 'force_rmse': 0.04}
calc.model.lossfunction = LossFunction(convergence=convergence,

Parallel processing

Most tasks in Amp are “embarrassingly parallel” and thus you should see a performance boost by specifying more cores. Our standard parallel processing approach requires the modules ZMQ (to pass messages between processes) and pxssh (to establish SSH connections across nodes, and is only needed if parallelizing on more than one node).

The code will try to automatically guess the parallel configuration from the environment variables that your batching system produces, using the function amp.utilities.assign_cores(). (We only use SLURM on our system, so we welcome patches to get this utility working on other systems!) If you want to override the automatic guess, use the cores keyword when initializing Amp. To specify serial operation, use cores=1; to specify (for example) 8 cores on only a single node, use cores=8 or cores={‘localhost’: 8}. For parallel operation, cores should be a dictionary where the keys are the hostnames and the values are the number of processors (cores) available on that node; e.g.,

cores = {'node241': 16,
         'node242': 16}

(One of the keys in the dictionary could also be localhost, as in the single-node example. Using localhost just prevents it from establishing an extra SSH connection.)

For this to work on multiple nodes, you need to be able to freely SSH between nodes on your system. Typically, this means that once you are logged in to your cluster you have public/private keys in use to ssh between nodes. If you can run ssh localhost without it asking you for a password, this is likely to work for you.

This also assumes that your environment is identical each time you SSH into a node; that is, all the packages such as ASE, Amp, ZMQ, etc., are available in the same version. Generally, if you are setting your environment with a .bashrc or .modules file this will just work. However, if you need to set your environment variables on the machine that is being ssh’d to, you can do so with the envcommand keyword, which you might set to

envcommand = 'export PYTHONPATH=/path/to/amp:$PYTHONPATH'

This envcommand can be passed as a keyword to the initialization of the Amp class. Ultimately, Amp stores these and passes them around in a configuration dictionary called parallel, so if you are calling descriptor or model functions directly you may need to construct this dictionary, which has the form parallel={‘cores’: ..., ‘envcommand’: ...}.

Advanced use

Under the hood, the train function is pretty simple; it just runs:

images = hash_images(images, ...)
self.descriptor.calculate_fingerprints(images, ...)
result =, self.descriptor, ...)
if result is True:
  • In the first line, the images are read and converted to a dictionary, addressed by a hash. This makes addressing the images simpler across modules and eliminates duplicate images. This also facilitates keeping a database of fingerprints, such that in future scripts you do not need to re-fingerprint images you have already encountered.
  • In the second line, the descriptor converts the images into fingerprints, one fingerprint per image. There are two possible modes a descriptor can operate in: “image-centered” in which one vector is produced per image, and “atom-centered” in which one vector is produced per atom. That is, in atom-centered mode the image’s fingerprint will be a list of lists. The resulting fingerprint is stored in self.descriptor.fingerprints, and the mode is stored in self.parameters.mode.
  • In the third line, the model (e.g., a neural network) is fit to the data. As it is passed a reference to self.descriptor, it has access to the fingerprints as well as the mode. Many options are available to customize this in terms of the loss function, the regression method, etc.
  • In the final pair of lines, if the target fit was achieved, the model is saved to disk.


If training is successful, Amp saves the parameters into an ‘<label>.amp’ file (by default the label is ‘amp’, so this file is ‘amp.amp’). You can load the pretrained calculator and re-train it further with tighter convergence criteria. You can specify if the pre-trained amp.amp will be overwritten by the re-trained one through the key word ‘overwrite’ (default is False).

calc = Amp.load('amp.amp')
calc.model.lossfunction = LossFunction(convergence=convergence)

If training does not succeed, Amp raises a TrainingConvergenceError. You can use this within your scripts to catch when training succeeds or fails, for example:

from amp.utilities import TrainingConvergenceError


except TrainingConvergenceError:
    # Whatever you want to happen if training fails;
    # e.g., refresh parameters and train again.

Global search in the parameter space

If the model is trained with minimizing a loss function which has a non-convex form, it might be desirable to perform a global search in the parameter space in prior to a gradient-descent optimization algorithm. That is, in the first step we do a random search in an area of parameter space including multiple basins (each basin has a local minimum). Next we take the parameters corresponding to the minimum loss function found, and start a gradient-descent optimization to find the local minimum of the basin found in the first step. Currently there exists a built-in global-search optimizer inside Amp which uses simulated-annealing algorithm. The module is based on the open-source simulated-annealing code of Wagner and Perry [1], but has been brought into the context of Amp. To use this module, the calculator object should be initiated as usual:

from amp import Amp
calc = Amp(descriptor=..., model=...)
images = ...

Then the calculator object and the images are passed to the Annealer module and the simulated-annealing search is performed by reducing the temperature from the initial maximum value Tmax to the final minimum value Tmin in number of steps steps:

from amp.utilities import Annealer
Annealer(calc=calc, images=images, Tmax=20, Tmin=1, steps=4000)

If Tmax takes a small value (greater than zero), then the algorithm reduces to the simple random-walk search. Finally the usual train() method is called to continue from the best parameters found in the last step: