Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials

Tian Xie¹, Arthur France-Lanord¹, Yanming Wang¹, Yang Shao-Horn¹, Jeffrey Grossman^1*

1 Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States

* Corresponding authors emails: jcg@mit.edu

DOI10.24435/materialscloud:2019.0017/v1 [version v1]

Publication date: May 09, 2019

How to cite this record

Tian Xie, Arthur France-Lanord, Yanming Wang, Yang Shao-Horn, Jeffrey Grossman, Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials, Materials Cloud Archive 2019.0017/v1 (2019), doi: 10.24435/materialscloud:2019.0017/v1.

Description

Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and environmental challenges. Many of these processes involve the dynamics of individual atoms or small molecules in condensed phases, e.g. lithium ions in electrolytes, water molecules in membranes, molten atoms at interfaces, etc., which are difficult to understand due to the complexity of local environments. We develop graph dynamical networks, an unsupervised learning approach for understanding atomic scale dynamics in arbitrary phases and environments from molecular dynamics simulations. We show that important dynamical information can be learned for various multi-component amorphous material systems, which is difficult to obtain otherwise. We develop a software package "gdynet" at https://github.com/txie-93/gdynet which implements the graph dynamical networks algorithm. This record contains the MD trajectories of a Li-S toy system, a Si-Au binary system, and a polymer battery electrolyte system in a format designed for the "gdynet" package.

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Files

File name	Size	Description
li2s-traj.npz MD5md5:5e935498b94c80c1da6394b06440e14d	23.2 MiB	A .npz file containing the molecular dynamics simulation trajectories of the Li-S toy system.
li2s-traj-graph-train.npz MD5md5:944a521962ff413bbb5aa310aaee3c7e	55.0 MiB	A .npz file containing the training data for the Li-S toy system after preprocessing.
li2s-traj-graph-val.npz MD5md5:f0a22d17649c7881da74dd13636b33e5	13.8 MiB	A .npz file containing the validation data for the Li-S toy system after preprocessing.
li2s-traj-graph-test.npz MD5md5:c672d02e5a7eaf2a37d311e1f1961c0a	68.7 MiB	A .npz file containing the testing data for the Li-S toy system after preprocessing.
siau-traj.npz MD5md5:055a3728605fee44372516f0a5a4cc8c	92.6 MiB	A .npz file containing the molecular dynamics simulation trajectories of the Si-Au binary system.
peo-traj-a.npz MD5md5:8fc46b6341f0fd5c1739542b30769572	1.9 GiB	A .npz file containing the molecular dynamics simulation trajectories of the PEO/LiTFSI binary system.
peo-traj-b.npz MD5md5:153e52b7cff0f43c6f5aed2a55e5ce59	1.9 GiB	A .npz file containing the molecular dynamics simulation trajectories of the PEO/LiTFSI binary system.
peo-traj-c.npz MD5md5:ad28b8ff20d1c1afd34ac1fe9a398092	1.9 GiB	A .npz file containing the molecular dynamics simulation trajectories of the PEO/LiTFSI binary system.
peo-traj-d.npz MD5md5:2c9abaf1fb491451349cde92b7b8bd4d	1.9 GiB	A .npz file containing the molecular dynamics simulation trajectories of the PEO/LiTFSI binary system.
peo-traj-e.npz MD5md5:43eaab6447ec4c32a18fbd25ed88d65e	1.9 GiB	A .npz file containing the molecular dynamics simulation trajectories of the PEO/LiTFSI binary system.
README.txt MD5md5:224a53617d0f6d2028b359eedee86479	2.0 KiB	README file for this data.

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution Non Commercial 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Preprint (Preprint where the data is discussed)

T. Xie, A. France-Lanord, Y. Wang, Y. Shao-Horn, J. Grossman, arXiv preprint arXiv:1902.06836 (2019)

Journal reference (Paper in which the data is discussed)

T. Xie, A. France-Lanord, Y. Wang, Y. Shao-Horn, J. Grossman, Nature Communications 10, 2667 (2019) doi:10.1038/s41467-019-10663-6

Keywords

machine learning molecular dynamics polymer deep learning graph neural networks amorphous interface trajectory

Version history:

2019.0017/v1 (version v1) [This version]	May 09, 2019	DOI10.24435/materialscloud:2019.0017/v1

Indexed by

materialscloud:2019.0017/v1