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.