Nearsighted force training

We have published a paper on the nearsighted force-training (NFT) approach, in which we used an ensemble-based atomic uncertainty metric to systematically generate small structures to address uncertain local chemical environments:

Zeng, Chen, and Peterson, “A nearsighted force-training approach to systematically generate training data for the machine learning of large atomic structures “. JCP 156, 064104 (2022). DOI:10.1063/5.0079314

We introduce a module to train an ensemle of bootstrap calculators in an active learning scheme, which aims to address uncertain local chemical environments in a large structure iteratively. We first train bootstrap calculators on an initial training set comprising of simple bulk structures. Next, we quantify atomic uncertainties on a large target structure, as the standard deviation of force predictions of the bootstrap calculators multiplied by a constant coefficient. We extract atomic “chunks” centered on the most uncertain atoms, and evaluated those “chunks” by single point calculations. We then extend the training set by the calculated “chunks”, and we retrain the bootstrap calculators until a certain stopping criterion is satisfied. For the retraining with atomic “chunks”, it is crucial that only the forces on central atoms are trained, which is the reason why this approach is termed as “nearsighted force training”.

Automatic protocol

The example script at below shows how to train bootstrap calculators based on the nearsighted force-training automatic protocol.

from amp.nft.activelearner import NFT
from amp.utilities import Logger

calc_text = """
from amp import Amp
from amp.model import LossFunction
from amp.descriptor.gaussian import Gaussian
from amp.model.neuralnetwork import NeuralNetwork

hl = [5, 5]
calc = Amp(model=NeuralNetwork(hiddenlayers=hl),
calc.model.lossfunction = LossFunction(convergence={'energy_rmse': 0.001,
                                                     'force_maxresid': 0.02})
al = NFT(stop_delta=0.02, max_iterations=20, steps_not_improved=2,

traj = 'initial_images.traj'
target_image = 'pt260.traj'
start_command = 'python', target_image=target_image, n=10,
       calc_text=calc_text, start_command=start_command,
       parent_calc=EMT(), cutoff=6.5)

Once the active learning is stopped, the bootstrap calculators giving the best results will be saved as best.[label].ensemble. The intermediate results will be saved inside the training folder in a folder named “saved-info”, which includes the trajectory and indices of selected atomic chunks , and atomic uncertainties of the target structure at each NFT iteration Indices and atomic uncertainties are saved in the ndarray format.

The active learning will be terminated if either condition at below is met—those conditions are supplied as parameters for the NFT class.

  • stop_delta controls the convergence structure uncertainty (maximum atomic uncertainty in the target structure).
  • max_iterations controls the maximum allowed number of NFT iterations.
  • steps_not_improved defines the number of consecutive NFT iterations to stop the NFT procedure if the structure uncertainty has not been improved.

The threshold controls the number of atomic “chunks” extracted from the target structure to be evaluated in single-point calculations. For example, threshold=-0.9 indicates that “chunks” with the top 10% atomic uncertainties will be calculated in electronic structures.

Calling run() method will spawn many independent training jobs, here n=10 jobs. Details of each job is given in the calc_text, and the jobs are submitted with the start_command. For details about those two parameters, we refer the readers to the documentation regarding the Bootstrap statistics. The initial bootstrap calculators are trained on initial_images.traj, and the uncertainty evaluation is targeted on pt260.traj. The parent_calc is the electronic structure method to perform single-point calculations on atomic “chunks”. The cutoff controls the range of atoms to be included in an atomic “chunk”.