From 9567a6ec4a5a598ba83c9771ce1eb8565a017ee6 Mon Sep 17 00:00:00 2001 From: Shyue Ping Ong Date: Tue, 14 May 2019 20:28:00 -0700 Subject: [PATCH] Add TOC. --- README.md | 25 +++++++++++++++++++++---- 1 file changed, 21 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 530cdbedb..0bce7c7ec 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,17 @@ [![CircleCI](https://circleci.com/gh/materialsvirtuallab/megnet.svg?style=svg)](https://circleci.com/gh/materialsvirtuallab/megnet) [![Coverage Status](https://coveralls.io/repos/github/materialsvirtuallab/megnet/badge.svg?branch=master)](https://coveralls.io/github/materialsvirtuallab/megnet?branch=master) +# Table of Contents +* [Introduction](#introduction) +* [MEGNet Framework](#megnet-framework) +* [Installation](#installation) +* [Usage](#usage) +* [Implementation details](#implementation-details) +* [Datasets](#datasets) +* [Computing requirements](#computing-requirements) +* [References](#references) + + # Introduction This repository represents the efforts of the [Materials Virtual Lab](http://www.materialsvirtuallab.org) @@ -13,7 +24,8 @@ suggestions are also welcome (please post on the Github Issues page.) A web app using our pre-trained MEGNet models for property prediction in crystals is available at http://megnet.crystals.ai. -# MatErials Graph Networks (MEGNet) for molecule/crystal property prediction + +# MEGNet framework The MatErials Graph Network (MEGNet) is an implementation of DeepMind's graph networks[1] for universal machine learning in materials science. We have @@ -39,6 +51,7 @@ output to a scalar/vector property. ![GraphModel architecture](resources/model_arch.jpg)
Figure 2. Schematic of MatErials Graph Network.
+ # Installation Megnet can be installed via pip for the latest stable version: @@ -53,6 +66,7 @@ For the latest dev version, please clone this repo and install using: python setup.py develop ``` + # Usage Our current implementation supports a variety of use cases for users with @@ -263,7 +277,7 @@ model.compile(loss='mse', optimizer='adam') With less than 20 lines of code, you have built a graph network model that is ready for materials property prediction! - + # Implementation details Graph networks[1] are a superclass of graph-based neural networks. There are a @@ -317,6 +331,7 @@ In summary the inputs for the model is **V** (1\*N'\*Nv), **E** (1\*M'\*Nm), **u** (1\*Ng\*Nu), `index1` (1\*M'), `index2` (1\*M'), `atom_ind` (1\*N'), and `bond_ind` (1\*M'). For Z-only atomic features, **V** is a (1\*N') vector. + # Data sets To aid others in reproducing (and improving on) our results, we have provided @@ -334,17 +349,19 @@ The molecule data set used in this work is the QM9 data set 30 processed by Faber et al.[6] It contains the B3LYP/6-31G(2df,p)-level DFT calculation results on 130,462 small organic molecules containing up to 9 heavy atoms. -# Computational requirements + +# Computing requirements Training: It should be noted that training MEGNet models, like other deep learning models, is fairly computationally intensive with large datasets. In our work, we use dedicated GPU resources to train MEGNet models with 100,000 -crystals/molecules. +crystals/molecules. It is recommended that you do the same. Prediction: Once trained, prediction using MEGNet models are fairly cheap. For example, the http://megnet.crystals.ai web app runs on a single hobby dyno on Heroku and provides the prediction for any crystal within seconds. + # References 1. Battaglia, P. W.; Hamrick, J. B.; Bapst, V.; Sanchez-Gonzalez, A.;