Materials Informatics for Beginners

Discovering new materials has traditionally taken years of trial-and-error in physics and chemistry labs. Materials informatics changes this by combining data science, machine learning, and materials chemistry to predict material properties and accelerate discovery. This written course introduces you to the intersection of materials science and data-driven computational methods. You will start with the foundational concepts of materials data, learning how crystal structures and chemical compositions are represented digitally. You will then explore how to leverage modern Python libraries, tidy dataframes, and basic machine learning models to analyze crystal structures, predict properties, and search databases for promising new compounds. What you'll learn: - Understand the core principles of materials informatics and digital data representation - Represent crystal structures and chemical formulas computationally using modern Python tools - Query public materials databases and extract structured datasets for analysis - Apply basic machine learning algorithms to predict physical and chemical properties of materials - Evaluate model performance using standard validation metrics in data science - Explore modern trends like generative molecular design and high-throughput screening workflows This course is structured to build your confidence step-by-step, starting with basic materials science definitions before moving on to hands-on computational exercises and data analysis workflows. You will read clear explanations, study structured code examples, and practice your skills through written conceptual checkpoints. This course is designed for beginners in materials science, chemistry, or physics who want to learn data-driven methods, as well as data enthusiasts interested in scientific applications. No prior background in machine learning or advanced programming is required. Start reading today and learn how to accelerate materials discovery with data science.