cheng7258002/elemnet

ElemNet is a 17-layered fully connected network for the prediction of formation energy (enthalpy) from elemental compositions only. This repository contains the model weights and a Jupiter notebook for making predictions using the ElemNet model.

ElemNet

This repository contains the analysis codes and deep learning models associated with "ElemNet: Deep Learning the Chemistry of Materials FromOnly Elemental Composition" by D. Jha et al. [ PDF].

Contents

The deep learning model produced in this work is available in the elemnet folder.

The other folders contain scripts associted with different analyses performed to characterize ElemNet. Each folder contains a README file that describes what the analyses are, and the notebooks should be self-describing.

Installation Requirements

As this git repository uses submodules, you need to clone it with git clone --recursive to gather all of the required source code.

The basic requirement for re-using these environments are a Python 3 Jupyter environment with the packages listed in requirements.txt.

Some analyses required the use of Magpie, which requires Java JDK 1.7 or greater. See [the Magpie documentation for details]

Citation

D. Jha, L. Ward, A. Paul, W.-keng Liao, A. Choudhary, C. Wolverton, and A. Agrawal, “ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition,” Scientific Reports, 8, Article number: 17593 (2018) [DOI:10.1038/s41598-018-35934-y] [PDF].