Coarse-Grained Modeling of Biomolecular Networks: A Practical Guide
A practical guide to coarse-grained modeling of biomolecular networks: bead mapping, interaction potentials, system equilibration, and validation against reference data.
ML-Biomat
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.
纤维生物·材料·结构的机器学习与多尺度模拟教程、研究笔记和实战指南。FiberNet 纤维网络设计 Python 工具包的官方网站。
🦮 ML for Science
🔬 Multiscale Methods
🦪 Fiber Biomaterials
📂 Categories / 分类
All →Machine Learning
机器学习
Multiscale Modeling多尺度模拟
Bio-Materials-Structures生物·材料·结构
Python TutorialsPython教程
Wet-lab Data Processing湿实验数据处理
English Articles
View all →Mechanical Testing of Biomaterials: A Reproducible Protocol Guide
A practical protocol-focused guide to reproducible mechanical testing of biomaterials, from specimen hydration and instrument setup to data reduction and a reporting checklist.
Setting Up a Python Scientific Computing Environment
A comprehensive three-platform guide covering virtual environments, Conda and Mamba, JupyterLab, and VS Code integration for materials science and biology graduate students.
Common Regression Models in Machine Learning
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.
中文文章
View all →生物分子网络的粗粒化建模：从映射方案到验证方法
面向材料研究的生物分子网络粗粒化建模指南，系统讲解珠子映射原则、有效相互作用势、体系构建与平衡、以及如何用独立参考数据验证模型，帮助研究者避免常见的粗粒化陷阱。
生物材料力学测试：试样制备、仪器设置与无伪影数据
面向湿实验研究的生物材料力学测试全流程指南，系统讲解试样水合、仪器架设、边界条件、应力应变规约、伪影识别与报告清单，帮助研究者获得可复现且可横向比较的力学参数。
Python 科学计算环境完全搭建指南：从零到生产力
从虚拟环境原理到Conda/Mamba实战再到Jupyter与VS Code配置，三平台全覆盖的Python科学计算环境搭建指南，面向生物与材料科学研究生的从零到生产力完整教程。
常用的机器学习回归模型：从线性基准到梯度提升
常用的机器学习回归模型：从线性基准到梯度提升。面向生物与材料科学研究生的实用教程，含完整可运行代码示例与详细原理解析，适合零基础入门与进阶参考。