xuliang2019 / orc-band

Orcband is a package used for predicting the band-gap of organic chemical compounds

Orc Band!

Project Introduction

Our overall goal of the project is to predict the bandgap of organic semiconductors with Machine Leaning Methods. The dataset that we use is Harvard Clean Energy Project Database. To achieve this goal, Our tasks are:

1. Calculate molecular descriptors for organic semiconductor from SMILES strings;
2. Determine predictors for mathine learning mehthod by LASSO regression;
3. Screen and optimize regression model;
4. Build a wrapping function that help user to use our model.

Calculation by RDkit

RDkit is a very useful and opensource package which can be download very easily. By using the map calculation in the package, we can easily get thousands of descriptors from the SMILES strings. And use several methods to screen the predictors.

Machine Learning Model

For all the regression models we choosed, 75% of the data are used to train the model and 25% are used to test the model. By choosing the model, we randomly choose a couple of small size of data to run it several times and calculate the average statistic data.

Multiple Linear Regression

Import Linear Regression by using

from sklearn.linear_model import LinearRegression

The score of this model is 0.59.

Polynominal Regression

Import Polynominal Regression by using

from sklearn.preprocessing import PolynomialFeatures

The score of this model is 0.54.

Random Forest regression

Import Random Forest Regression by using

from sklearn.ensemble import RandomForestRegressor

The score of this model is 0.67.

Neural Network

Import Keras by using

from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor

The score of this model is 0.57.

*Note: Tensor Flow Needed!

Prediction Model

The Scatter Figure of the Predicted Bandgap for 4 Models are as follows.

The calculated statistic data are as follows:

Error	Multiple Linear	Random Forest	Polynomial	Neural Network
MSE	0.0450	0.0357	0.0503	0.1732
MAE	0.1659	0.1425	0.1728	0.3349
MAPE	0.0928	0.0792	0.0959	0.1883
$R^2$	0.5933	0.6772	0.5458	0.5665
Kfold	0.5906	0.6819	0.5906	-0.0620

According to the figure and the table above, we choose the Random Forest Regression as our Prediction Model. And by optimizing it, we have a really good model, which has $R^2=0.80$. (For whole data set)

Installation

1.Rdkit

$ conda create -c rdkit -n my-rdkit-env rdkit

This is recommended and we install this in this way. Here is the link for rdkit Note: Rdkit package muse be installed in your computer, and you may need to download it manually by yourself

2.Orcband

1. $ pip install git+https://github.com/xuliang2019/Orc_Band.git

2.Orcband Instruction

*Note: The data we use is confidential, so you may be not able to run our jupyter notebook directly in each file.