# polySML **Repository Path**: polysml/polySML ## Basic Information - **Project Name**: polySML - **Description**: This suite of standalone software is to predict mechanical, thermal, conductivity, filtration and separational etc. properties for variant polymer materials. - **Primary Language**: Python - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 2 - **Forks**: 2 - **Created**: 2020-07-21 - **Last Updated**: 2020-12-29 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # polySML: structure & machine learning for polymer materials This suite of standalone software is to predict mechanical, thermal, conductivity, filtration and separational etc. properties for variant polymer materials. ## Requirements Win7/vista/win8/win10, 105M RAM, 400M storage ## Usage This model provides the prediction for three critical performance indexes accounting micro-/ultra-/nano-filtration membranes and the evaluation of the overall membrane performance grade. 1. Input correct **SMILES** for addictive in polymer solution (SMILES can be queried from Pubchem https://pubchem.ncbi.nlm.nih.gov/ ). ![image-20200710230313673](./imgs/smile.bmp) 2. Parameters for **composition, fabrication** and **test condition**. ![image-20200710230313673](./imgs/composition.bmp) ![image-20200710230317425](./imgs/fabrication.bmp) ![image-20200710230325122](./imgs/test.bmp) 3. Click **Submit** and check all the inputs correct. 4. Click **GO** for predictions. ![image-20200710230335645](./imgs/result.bmp) 5. Click **Save As** to save outputs. ![image-20200710230341904](./imgs/save.bmp) ## Predictor: WFM_poly This model provides the prediction for three critical performance indexes accounting micro-/ultra-/nano-filtration membranes and the evaluation of the overall membrane performance grade. ### Regression models #### Permeability(Lp) The permeability of membrane is defined as: $$ Lp = J_{v}/(\Delta P \times p_{0}) $$ Where $J_{v}$ is the volumetric filtrate flux (m/s), ΔP is the transmembrane pressure (Pa), $p_{0}$ is the unit permeability coefficient (1 $ms^{-1}Pa^{-1}$) #### Selectivity($1/S_{0}$) The selectivity of membrane is defined as: $$ 1/S_0=1/(1-R)=C_f/C_p $$ Where R is the rejection ratio (%), $C_{p}$ and $C_{f}$ are the concentrations of substances in the permeation and feed flux (wt%). #### Trade-off coefficient(Tr) The trade-off coefficient of membrane is defined as: $$ T_r=(2×Lp×1/S_0)/(Lp+1/S_0 ) $$ ### Classification models According to the "trade-off curve", we got four curves which can divide the macromolecules and salts data points into 50:50 and 20:80. The output result is "Y" means that the performance of the membrane is above the trade-off curve, on the contrary, the result is "N". #### **Features** Features used for prediction models are listed below: | **ID** | **Feature** | **Unit** | **Description** | | ------ | ------------- | -------- | ------------------------------------------------------------ | | **1** | p_C | wt% | The weight fraction of base polymer in casting solution | | **2** | a_C | wt% | The weight fraction of the addictive in casting solution | | **3** | s_C | wt% | The weight fraction of the solvent in casting solution | | **4** | s_Disp | | Hansen solubility parameter (Dispersion force for solvent) | | **5** | p_Disp | | Hansen solubility parameter (Dispersion force for monomer in polymer) | | **6** | RED_S | | The relative energy difference between base polymer and solvent | | **7** | RED_NS | | The relative energy difference between base polymer and non-solvent | | **8** | Bp | $^{o}C$ | Boiling point for solvent and non-solvent | | **9** | Vp | mmHg | The saturated vapor pressure at 25oC for solvent and non-solvent | | **10** | HDT | $^{o}C$ | Heat Deflection Temperature with loading of 1.8MPa | | **11** | coag_T | $^{o}C$ | The temperature of coagulation bath | | **12** | pre_T | $^{o}C$ | The temperature during membrane formation | | **13** | exposed.time | s | The exposed time before immersing the casting solution into the non-solvent | | **14** | wet_mem_thick | μm | The thickness of solution on the substrate controlled by the scraper | | **15** | flux_P | kPa | Transmembrane pressure in performance measurement | | **16** | rej_C | wt% | The concentration of substance (protein, salt etc,) in feeding flux | | **17** | rej_type | | The type of separation substance | | **18** | rej_charge | C | The charge of separation substance | | **19** | rej_r | nm | The radius of rejection substance | | **20** | porosity | % | Volume fraction of water accessible voids in membrane | | **21** | CA | $^{o}$ | Water static contact angle on membrane surface | ## License The copyright for this software suite is owe to the authors, academic free for current version and commercial usages please contact the corresponding author yunqi@ciac.ac.cn. ## References User are encouraged to cite the following references for special predictors. 1. Liu T, Liu L, Cui F, Ding F, & Li Y Predict the performance of polyvinylidene fluoride, polyethersulfone and polysulfone micro/ultra/nano-filtration membranes, 2020, submitted. 2. Liu L, Chen W, Liu T, Kong X, Zheng J, & Li Y Rational design of hydrocarbon-based sulfonated copolymers for proton exchange membranes J. Mater. Chem. A, 2019 7:11847-11857. 3. Liu L, Chen W, & Li Y A Statistical Study of Proton Conduction in Nafion?-based Composite Membranes: Prediction, Filler Selection and Fabrication Methods J. Membr. Sci., 2018 549:393-402. 4. Liu L, Chen W, & Li Y An overview of the proton conductivity of nafion membranes through a statistical analysis J. Membr. Sci., 2016 504:1-9. **Release log:** 201912 version v0, construct the UI and common block. WFM_poly model was integrated. 202003 version v1£¬predictor about polymer materials for water filtration membranes(WFM_poly) was integrated. ## Limitation Current models mainly focus on the types of polymers, additions, solvents reported. For novel compounds, chemical structures, predictions are made based on knowledge, confidence needs validation in blind-test. We are not guarantee the prediction is fully accurate but guidelines are possible. ## Bug report and suggestions Please contact either lyliu@ciac.ac.cn or yunqi@ciac.ac.cn for bugs or suggestions. ## About us We are a research group didicate in structure and machine learning study on polymer materials. We are welcoming suggestions and collaborations. Contact Prof. Yunqi Li for further information.