Educational materials, tools, and learning content
Predict properties instantly right here in this panel!
Our machine learning models let you estimate key properties from molecular structure alone.
Click any property below, enter a SMILES string, and get your prediction in seconds.
For detailed guides on SMILES notation, models, and more, visit the PolymatAI website.
Reactivity Ratios
Propagation Rate
Glass Transition Temp
Water Solubility
A comprehensive course on Autodesk Moldflow for plastic injection molding simulation, covering 22 sessions across 5 chapters (~7.5 hours). Topics include software installation and interface overview, injection molding process fundamentals, part design using Inventor, SolidWorks, Siemens NX, and CATIA, pre-processing setup including meshing, material selection, gate location, and process settings, as well as fill & pack analysis, runner and cooling system design, warpage & shrinkage analysis, and multi-cavity systems. This course is taught in Persian (Farsi).
Visit Full Course WebsiteLearn how to apply machine learning techniques to polymer science problems. Covers data preprocessing, model selection, training, and validation with practical examples from emulsion polymerization.
Introduction to Python programming with focus on chemical engineering applications. Includes data analysis, visualization, numerical methods, and process simulation.
Solved problems covering key polymerization concepts • PDF • 243 KB
Summary Slides Based on Oshima & Hogue's Book • PPTX • 12.1 MB
Solving game examples with RL in Jupyter notebooks • RAR • 1.1 MB
Excel template for data collection • XLSX • 1.8 MB
Open-source code from my research projects. Includes Python libraries, machine learning models, data processing scripts, and visualization tools.
Custom Python packages for polymer science and machine learning applications. Documentation and examples included.
Curated datasets from different polymerization experiments. Available for research and educational purposes with proper attribution.