Journal of Chemical Information and Modeling, volume 60, issue 3, pages 1184-1193

PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials

Shao Yunqi 1
Knijff Lisanne 1
Publication typeJournal Article
Publication date2020-01-14
Quartile SCImago
Q1
Quartile WOS
Q1
Impact factor5.6
ISSN15499596, 1549960X
General Chemistry
Computer Science Applications
General Chemical Engineering
Library and Information Sciences
Abstract
Atomic neural networks (ANNs) constitute a class of machine learning methods for predicting potential energy surfaces and physico-chemical properties of molecules and materials. Despite many successes, developing interpretable ANN architectures and implementing existing ones efficiently are still challenging. This calls for reliable, general-purpose and open-source codes. Here, we present a python library named PiNN as a solution toward this goal. In PiNN, we designed a new interpretable and high-performing graph convolutional neural network variant, PiNet, as well as implemented the established Behler-Parrinello high-dimensional neural network. These implementations were tested using datasets of isolated small molecules, crystalline materials, liquid water and an aqueous alkaline electrolyte. PiNN comes with a visualizer called PiNNBoard to extract chemical insight ``learned'' by ANNs, provides analytical stress tensor calculations and interfaces to both the Atomic Simulation Environment and a development version of the Amsterdam Modeling Suite. Moreover, PiNN is highly modularized which makes it useful not only as a standalone package but also as a chain of tools to develop and to implement novel ANNs. The code is distributed under a permissive BSD license and is freely accessible at \href{https://github.com/Teoroo-CMC/PiNN/}{https://github.com/Teoroo-CMC/PiNN/} with full documentation and tutorials.

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Shao Y. et al. PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials // Journal of Chemical Information and Modeling. 2020. Vol. 60. No. 3. pp. 1184-1193.
GOST all authors (up to 50) Copy
Shao Y., Hellström M., Mitev P., Knijff L., Zhang C. PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials // Journal of Chemical Information and Modeling. 2020. Vol. 60. No. 3. pp. 1184-1193.
RIS |
Cite this
RIS Copy
TY - JOUR
DO - 10.1021/acs.jcim.9b00994
UR - https://doi.org/10.1021%2Facs.jcim.9b00994
TI - PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials
T2 - Journal of Chemical Information and Modeling
AU - Knijff, Lisanne
AU - Hellström, Matti
AU - Mitev, Pavlin Dakev
AU - Zhang, Chao
AU - Shao, Yunqi
PY - 2020
DA - 2020/01/14 00:00:00
PB - American Chemical Society (ACS)
SP - 1184-1193
IS - 3
VL - 60
SN - 1549-9596
SN - 1549-960X
ER -
BibTex |
Cite this
BibTex Copy
@article{2020_Shao
author = {Lisanne Knijff and Matti Hellström and Pavlin Dakev Mitev and Chao Zhang and Yunqi Shao},
title = {PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials},
journal = {Journal of Chemical Information and Modeling},
year = {2020},
volume = {60},
publisher = {American Chemical Society (ACS)},
month = {jan},
url = {https://doi.org/10.1021%2Facs.jcim.9b00994},
number = {3},
pages = {1184--1193},
doi = {10.1021/acs.jcim.9b00994}
}
MLA
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MLA Copy
Shao, Yunqi, et al. “PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials.” Journal of Chemical Information and Modeling, vol. 60, no. 3, Jan. 2020, pp. 1184-1193. https://doi.org/10.1021%2Facs.jcim.9b00994.
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