Introduction

Materials science is a rapidly evolving field that has the potential to transform many industries, from healthcare to energy and beyond. In recent years, there has been an explosion of interest in the use of machine learning (ML) techniques to accelerate the discovery, development, and analysis of new materials. In this workshop, we to aim to teach basic skills in scientific computing, data analysis, and machine learning, and to show how these skills can be applied in the areas of solid state physics and materials science.

The use of ML in materials science is not a new concept, but recent advances in hardware and software have made it possible to apply these techniques to larger and more complex datasets. ML algorithms can now be used to predict the properties of materials based on their chemical composition, crystal structure, and other characteristics. This has the potential to significantly reduce the time and cost of traditional trial-and-error approaches to material development.

What will we be covering?

In this workshop, we will first provide a brief survey of scientific computing using the Python programming language, covering some of the most commonly used Python packages such as Numpy, Scipy, and Matplotlib. Through a series of hands-on Python tutorials, we will help develop skills in data analysis, data visualization, and array-based computing. Next, we will explore many of the ML techniques used in materials science, including supervised learning, unsupervised learning, and deep learning. We will also give hands-on tutorials applying several different machine learning models, such as regression models, kernel machines, and deep neural networks. Finally, we will apply these models to real research problems in materials science and solid state physics.

This workshop is intended for researchers and practitioners in the field of materials science who are interested in developing their skills in scientific computing and ML, and how these skills can be applied to their work. It is also suitable for students and academics who are interested in the intersection of these two fields.

We hope that this workshop will inspire others to explore the use of ML and scientific computing in their own work and contribute to the continued growth of this exciting field.