Tutorials, research notes, and practical guides on ML and multiscale modeling for fiber-based biomaterials. Home of FiberNet, a Python toolkit for fiber network design and optimization.
Tutorials, research notes, and practical guides on ML and multiscale modeling for fiber-based biomaterials. Home of FiberNet, a Python toolkit for fiber network design and optimization.
A comprehensive three-platform guide covering virtual environments, Conda and Mamba, JupyterLab, and VS Code integration for materials science and biology graduate students.
A systematic guide to linear regression, Ridge, Lasso, polynomial regression, SVR, decision trees, random forests, and gradient boosting for scientific and engineering applications, with complete code examples.
A systematic introduction to deep learning: neurons, activation functions, backpropagation, regularization, and transfer learning, with practical guidance on when deep learning is the right tool in materials science.
A complete hands-on introduction to NetworkX: from graph theory fundamentals to network construction, analysis metrics, and visualization.
A systematic overview of common ML classification models, from logistic regression to gradient boosting, with principles, code, and comparison.
A comprehensive review of engineered superstructures: origami, honeycomb, auxetic, helical, and lattice designs in 2D and 3D, covering geometry, mechanisms, parameters, and design rules.
A navigational guide to hierarchical superstructures in biomaterials: silk fibroin, collagen, bone, nacre, spider silk, and cellulose analyzed through their structure-mechanics relationships.
A complete hands-on tutorial for GROMACS molecular dynamics simulation, covering installation, system preparation, energy minimization, equilibration, production MD, and trajectory analysis (RMSD/RMSF/Rg/H-bonds) using lysozyme as a worked example.
A beginner-friendly guide to interpreting stress-strain curves: elastic modulus, yield strength, ultimate strength, toughness, and how these concepts apply to soft biomaterials.
How silk fibroin's hierarchical structure—from amino acid sequence to beta-sheet crystallites—dictates its remarkable mechanical properties and guides biomaterials design.
How to analyze SEM images of fiber networks with Python: thresholding, fiber measurement, orientation analysis, and batch processing using scikit-image.
A comprehensive guide to curve fitting with SciPy for materials and biology researchers. Linear regression, exponential decay, Hertz indentation, viscoelastic models, with complete code and real examples.