Data and code underlying the PhD thesis: Data-driven methods to design, learn, and interpret complex materials across scales

This repository contains code and data related to the underlying PhD thesis: Data-driven methods to design, learn, and interpret complex materials across scales. The repository is divided into the individual codes and datasets of each chapter. <strong>Chapter 2</strong> explores the inverse design of 2D metamaterials for elastic properties, utilizing machine learning techniques to optimize material structure and performance. <strong>Chapter 3</strong> focuses on learning hyperelastic material models without relying on stress data, employing data-driven approaches to predict material behavior under large strains. <strong>Chapter 4</strong> extends this by developing interpretable hyperelastic material models, ensuring both accuracy and physical consistency without stress data. <strong>Chapter 5</strong> explores the inverse design of 3D metamaterials under finite strains and applies novel ML frameworks to design these complex material structures. <strong>Chapter 6</strong> investigates the use of deep learning to uncover key predictors of thermal conductivity in covalent organic frameworks (COFs) and reveals new insights into the relationship between molecular structure and thermal transport. <strong>Chapter 7</strong> introduces a graph grammar-based approach for generating novel polymers in data-scarce settings, thus combines computational design with minimal data.

本仓库收录了与下述博士论文相关的代码与数据集：《跨尺度复杂材料的设计、学习与可解释数据驱动方法》。本仓库按各章节划分了独立的代码与数据集。第2章围绕弹性性能二维超材料的逆向设计展开研究，借助机器学习技术优化材料结构与性能表现。第3章聚焦于无需应力数据的超弹性材料模型学习，采用数据驱动方法预测大应变下的材料力学行为。第4章在此基础上开发了可解释超弹性材料模型，在无需应力数据的前提下同时保证模型精度与物理一致性。第5章研究有限应变下三维超材料的逆向设计，并应用新型机器学习框架完成此类复杂材料结构的设计。第6章探究了利用深度学习揭示共价有机框架（Covalent Organic Frameworks, COFs）导热系数关键影响因子的方法，并为分子结构与热传输之间的关联提供了全新认知。第7章提出了一种基于图语法的方法，用于数据稀缺场景下新型聚合物的生成，从而实现了仅依赖少量数据的计算设计。