From 7094fd64df19b7807b2b08b15c539078f4f6ad69 Mon Sep 17 00:00:00 2001 From: Charles Le Losq Date: Mon, 30 Aug 2021 09:49:38 +0200 Subject: [PATCH] Update readme for reference #2 --- Prediction_simple.ipynb | 15 ++------------- README.md | 13 +++++++++++-- 2 files changed, 13 insertions(+), 15 deletions(-) diff --git a/Prediction_simple.ipynb b/Prediction_simple.ipynb index 256c339..22b501c 100644 --- a/Prediction_simple.ipynb +++ b/Prediction_simple.ipynb @@ -2,7 +2,6 @@ "cells": [ { "cell_type": "markdown", - "id": "5355fc42", "metadata": {}, "source": [ "# Prediction for one composition\n", @@ -13,7 +12,6 @@ { "cell_type": "code", "execution_count": 1, - "id": "d4b12fec", "metadata": {}, "outputs": [ { @@ -63,7 +61,6 @@ }, { "cell_type": "markdown", - "id": "5ec8e957", "metadata": {}, "source": [ "# What is the composition of interest ?\n", @@ -74,7 +71,6 @@ { "cell_type": "code", "execution_count": 2, - "id": "abc78aa0", "metadata": {}, "outputs": [], "source": [ @@ -83,7 +79,6 @@ }, { "cell_type": "markdown", - "id": "a8c73ac2", "metadata": {}, "source": [ "# Viscosity prediction\n", @@ -94,7 +89,6 @@ { "cell_type": "code", "execution_count": 3, - "id": "549bdc39", "metadata": {}, "outputs": [], "source": [ @@ -104,7 +98,6 @@ }, { "cell_type": "markdown", - "id": "d4aabe46", "metadata": {}, "source": [ "2/ select the equation, among: \n", @@ -119,7 +112,6 @@ { "cell_type": "code", "execution_count": 4, - "id": "356de370", "metadata": {}, "outputs": [], "source": [ @@ -128,7 +120,6 @@ }, { "cell_type": "markdown", - "id": "a71abaf2", "metadata": {}, "source": [ "# Predictions\n", @@ -139,7 +130,6 @@ { "cell_type": "code", "execution_count": 5, - "id": "4e2eb8bd", "metadata": {}, "outputs": [ { @@ -164,7 +154,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -218,7 +208,6 @@ { "cell_type": "code", "execution_count": null, - "id": "6e8598c3", "metadata": {}, "outputs": [], "source": [] @@ -240,7 +229,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.7.7" } }, "nbformat": 4, diff --git a/README.md b/README.md index c145f9e..f7010fd 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,10 @@ (c) 2021 Charles Le Losq, lelosq@ipgp.fr +i-Melt is a physics-guided neural network model, that combines deep neural networks with physical equations to predict the structural, thermodynamic and dynamic properties of aluminosilicate melts and glasses. + +Please see the original publication Le Losq et al. (2021) for details: https://www.sciencedirect.com/science/article/abs/pii/S0016703721005007 + ## LICENSE Any material in this repository is under the MIT licence @@ -48,7 +52,7 @@ The code **Training_Candidates.py** allows training 100 networks with the refere Analysis of results and predictions from the 10 best trained networks is done in several steps: -- The **Results_experiments.ipynb** code allows observing the results of the random search and dataset size experiments in Supplementary Figure 1 (see ./figures folder). **Please note that the originally trained networks for experiments 1 and 2 take too much size and are thus not provided via this repository. Running this notebook thus requires to first run the codes Experiment_1_architecture.py AND Experiment_2_dataset_size.py (see above)** +- The **Results_experiments.ipynb** code allows observing the results of the random search and dataset size experiments in Supplementary Figure 1 (see ./figures folder). **Please note that the originally trained networks for experiments 1 and 2 take too much size and are thus not provided via this repository. Running this notebook thus requires to first run the codes Experiment_1_architecture.py AND Experiment_2_dataset_size.py (see above) ** - The **Results_model_performance.ipynb** makes a statistical analysis of the performance of the bagged 10 best models. @@ -62,4 +66,9 @@ The code Training_single.ipynb allows training only one network and playing with ### Getting predictions from the 10 best networks from Le Losq et al. 2021 -The notebook Prediction_simple.ipynb allows to get predictions for a given melt composition. Open a Jupyter notebook server, open the notebook and follow the instructions! \ No newline at end of file +The notebook Prediction_simple.ipynb allows to get predictions for a given melt composition. Open a Jupyter notebook server, open the notebook and follow the instructions!** + +## References + +Le Losq C., Valentine A., Mysen B. O., Neuville D. R., 2021. Structure and properties of alkali aluminosilicate glasses and melts: insights from deep learning. Geochimica and Cosmochimica Acta, https://doi.org/10.1016/j.gca.2021.08.023 +