Data-Based Methods for Materials Design and Discovery

Discover the transformative power of machine learning in materials science with Data-Based Methods for Materials Design and Discovery by Ghanshyam Pilania. Published by Springer International Publishing AG in 2020, this insightful paperback spans 172 pages and delves into innovative approaches for tackling complex materials challenges, including alloys, ferroelectrics, and dielectrics.

This book emphasizes the application of probabilistic methods, particularly Gaussian processes, to effectively estimate density functions. Whether you are a researcher, student, or industry professional, this comprehensive guide offers valuable insights into the latest advancements in materials design and discovery. Enhance your understanding of data-driven techniques that are reshaping the field and learn how to leverage these methods for your own projects.

Unlock the potential of modern materials research with this essential resource!