pantea 0.7.4

JAX-based Interatomic Potential

An optimized Python package for developing machine learning interatomic potentials

Description

Pantea is an optimized Python library on basis of Google JAX that enables development of machine learning inter-atomic potentials for use in computational physics, chemistry, and material science. These potentials are necessary for conducting large-scale molecular dynamics simulations of complex materials with ab initio accuracy.

See documentation for more information.

Features

• The design of Pantea is simple and flexible, which makes it easy to incorporate atomic descriptors and potentials.

• It uses automatic differentiation to make defining new descriptors straightforward.

• Pantea is written purely in Python and optimized with just-in-time (JIT) compilation.

• It also supports GPU-accelerated computing, which can significantly speed up preprocessing and model training.

Warning

This package is under heavy development and the current focus is on the implementation of high-dimensional neural network potential (HDNNP) proposed by Behler et al. (2007).

Installation

To install Pantea, run this command in your terminal:

$ pip install pantea

For machines with an NVIDIA GPU please follow the installation instruction on the documentation.

Examples

Defining an ACSF descriptor

This script demonstrates the process of evaluating an array of atomic-centered symmetry functions (ACSF) for a specific element, which can be utilized to evaluate the descriptor values for any structure. The resulting values can then be used to construct a machine learning potential.

from pantea.datasets import RunnerDataset
from pantea.descriptors import ACSF
from pantea.descriptors.acsf import CutoffFunction, G2, G3

# Read atomic structure dataset (e.g. water molecules)
structures = RunnerDataset('input.data')
structure = structures[0]
print(structure)
# >> Structure(natoms=12, elements=('H', 'O'), dtype=float32)

# Define an ACSF descriptor for hydrogen element
# It includes two radial (G2) and angular (G3) symmetry functions
descriptor = ACSF('H')
cfn = CutoffFunction.from_cutoff_type(r_cutoff=12.0, cutoff_type='tanh')
descriptor.add(G2(cfn, eta=0.5, r_shift=0.0), 'H')
descriptor.add(G3(cfn, eta=0.001, zeta=2.0, lambda0=1.0, r_shift=12.0), 'H', 'O')
print(descriptor)
# >> ACSF(central_element='H', symmetry_functions=2)

values = descriptor(structure)
print("Descriptor values:\n", values)
# >> Descriptor values:
# [[0.01952942 1.1310327 ]
# [0.01952756 1.0431229 ]
# ...
# [0.00228752 0.4144546 ]]

gradient = descriptor.grad(structure, atom_index=0)
print("Descriptor gradient:\n", gradient)
# >> Descriptor gradient:
# [[ 0.0464524  -0.05037863 -0.06146219]
# [-0.10481848 -0.01841717  0.04760207]]

Training an NNP potential

This example illustrates how to quickly create a high-dimensional neural network potential (HDNNP) instance from an in input setting files and train it on input structures. The trained potential can then be used to evaluate the energy and force components for new structures.

from pantea.datasets import RunnerDataset
from pantea.potentials import NeuralNetworkPotential

# Read atomic data in RuNNer format
structures = RunnerDataset("input.data")
structure = structures[0]

nnp = NeuralNetworkPotential.from_file("input.nn")

nnp.fit_scaler(structures)
nnp.fit_model(structures)
# nnp.save()
# nnp.load()

total_energy = nnp(structure)
print(total_energy)
# >> -15.386198

forces = nnp.compute_forces(structure)
print(forces)
# >> [[ 1.6445214e-02 -4.1671786e-03  7.6140024e-02]
# [-6.4949177e-02 -4.2048577e-02  5.6018140e-02]
# ...
# [ 7.6149488e-03 -9.5360324e-02 -9.2892153e-03]]

Example files: input.data and input.nn

License

This project is licensed under the GNU General Public License (GPL) version 3 - see the LICENSE file for details.