Crystal structure prediction in materials discovery

Introduction

This 3-day workshop will broadly cover four following themes focusing on the challenges and opportunities in Crystal Structure Prediction (CSP):

Recent advances in CSP methods
Examples of CSP application for molecular crystals
Examples of CSP application for inorganic crystals
Where can Machine Learning help CSP

We will identify common challenges and enabling approaches across different material classes and discuss the opportunities and best practice in the field of CSP. Since the challenges are often of inter-disciplinary nature, we aim to bring together experts in materials science (organic, inorganic and hybrid materials) and computer science (machine learning, optimisation). Specifically, the workshop will be devoted to discussing the following important aspects of the field:

How to use CSP to accelerate design of functional materials
How to construct the search space to conduct high-throughput calculations
How to parameterise fast chemical models such as forcefields and machine learning models using first-principles and experimental data
How to usefully use the information from high-throughput calculations
What can the materials science community and molecular crystals community learn from each other

The theoretical and computational challenges in the CSP will be discussed, with a focus on energy materials and pharmaceuticals.