Polymer Multiobjective Bayesian Optimization (PMBO) assiciated by physical descriptors
Polymer Multiobjective Bayesian Optimization (PMBO) framework for exploring multifunctional polymers, including four components: 1) Polymer Library, which stores all polymer candidates; 2) Feature extraction, which based on physical feature engineering or graph descriptors generation; 3) Polymer properties simulator, which calculates polymer properties at different scales using DFT, MD and ML. 4) Multi-objective Bayesian algorithm, which predicts polymer properties and recommends potentially multifunctional polymers.Please refer to our work "Discovery of Multifunctional Polymers Via Physical Descriptors-Guided Multi-objective Bayesian Optimization" for additional details.
To download, clone this repository:
git clone https://github.com/SJTU-MI/PMBO.git
To run most code in this repository, the relevant anaconda environment can be installed from environment.yml. To build this environment, run：
cd ./PMBO
conda env create -f environment.yml
conda activate pmbo
Additionally, polymer physical feature engineering and properties calculations can be accessed at other GitHub repositories of APFEforPI and RadonPy.
gp.py: Gaussian process regression model
Acq_fun.py: Acquisition functions such as EI(expected improvement) and UCB(upper confidence bound)
hypervolume.py: Calculation of hypervolume
utility.py: Utility functions such as Pareto front allocation and data pre/post processing
optimize.py: Core of multi-objective Bayesian optimization
log.py: PMBO Logo
MBO_tutorial.ipynb: A case of multi-objective optimization for multifunctional polymers discovery
example.csv:Benchmark dataset for testing (input file)
cal_data.csv: MBO recommended polymers and their observed properties (output file)
HV.csv: Optimized convergence curve evaluated by hypervolume (output file)
| AUTHORS | Xiang Huang, Shenghong Ju |
|---|---|
| VERSION | V1.0 / July,2023 |
| EMAILS | [email protected] |
| GROUP HOME | https://ju.sjtu.edu.cn/en/ |
This work is under BSD-2-Clause License. Please, acknowledge use of this work with the appropiate citation to the repository and research article.