Introduction

PiNN is a Python library built on top of TensorFlow for building atomic neural networks (ANNs). The primary usage of PiNN is to build and train ANN interatomic potentials, but PiNN is also capable of predicting physical and chemical properties of molecules and materials.

Flexibility

PiNN is built with modularized components, and we try to make it as easy as possible. You do not have to rewrite everything if you just want to design a new network structure, or apply an existing network to new datasets or new properties.

Scalability

PiNN fully adheres to TensorFlow's high-level Estimator and Dataset API. It is straightforward to train and predict on different computing platforms (CPU, multiple-GPU, cloud, etc.) without explicit programming.

Examples

The quickest way to start with PiNN is to follow our example notebooks. The notebooks provide guides to train a simple ANN potential with a public dataset, import your own data or further customize the PiNN for your need.

Cite PiNN

If you find PiNN useful, welcome to cite it as:

Y. Shao, M. Hellström, P. D. Mitev, L. Knijff, and C. Zhang. PiNN: a python library for building atomic neural networks of molecules and materials. J. Chem. Inf. Model., 60:1184–1193, January 2020. doi:10.1021/acs.jcim.9b00994.

Bibtex

@Article{2020_ShaoHellstroemEtAl,
  author    = {Yunqi Shao and Matti Hellström and Pavlin D. Mitev and Lisanne Knijff and Chao Zhang},
  journal   = {J. Chem. Inf. Model.},
  title     = {{PiNN}: A Python Library for Building Atomic Neural Networks of Molecules and Materials},
  year      = {2020},
  month     = {jan},
  number    = {3},
  pages     = {1184--1193},
  volume    = {60},
  doi       = {10.1021/acs.jcim.9b00994},
  publisher = {American Chemical Society ({ACS})},
}