Machine Learning for Materials Informatics

Harness Machine Learning to Revolutionize Materials Informatics

In today's fast-evolving world of materials science, artificial intelligence and machine learning are transforming the way researchers and engineers design, model, and discover new materials. Machine Learning for Materials Informatics: Use ML Tools for Visualization, Multiscale Modeling and Discovery is your ultimate guide to applying AI-driven methods for computational materials design and predictive modeling.

This book bridges the gap between traditional materials science and advanced ML techniques, offering practical strategies, real-world case studies, and implementation insights for accelerating materials discovery. Whether you are a student, researcher, or industry professional, this resource equips you with the knowledge to use ML for intelligent material design, analysis, and optimization.

Inside this comprehensive guide, you will learn:

Fundamentals Of Materials Informatics And AI Integration – Understand the core principles of materials informatics, data-driven modeling, and how AI reshapes the traditional approach to materials science for faster, more accurate predictions.

Machine Learning Techniques Tailored For Materials Science – Explore supervised, unsupervised, and reinforcement learning approaches, and learn how regression models, neural networks, and ensemble methods apply to complex material systems.

Advanced Visualization And Feature Engineering For Materials Data – Discover how to extract meaningful features, handle large-scale datasets, and utilize visualization tools to interpret high-dimensional material properties effectively.

Multiscale Modeling And Simulation Using ML – Gain expertise in integrating machine learning with atomistic, mesoscale, and continuum-level modeling to achieve accurate multiscale simulations for materials design and property prediction.

AI-Driven Materials Discovery And Property Prediction – Learn how cutting-edge ML algorithms accelerate the discovery of novel alloys, polymers, and composites while predicting key properties such as conductivity, strength, and durability.

Practical Implementation With Python And Popular Frameworks – Follow step-by-step examples using libraries like Scikit-learn, TensorFlow, and PyTorch to build predictive models, optimize workflows, and deploy real-world solutions in computational materials science.