Nowadays, the urgent demand for new technologies has greatly exceeds the capabilities of materials research. Thanks to the spectacular progress in high-performance computing, computational modelling has been playing an increasingly important role in accelerating materials discovery and innovation. Understanding the atomic structure of a material is the first step in computational materials design. In the past years, we have been focusing on the development of first-principle structure predictions, based on evolutionary algorithms (EA), and apply it study a variety of to materials from bulk crystals, surfaces, materials defects. When the structure model is available, we employ approximate methods within the framework of density functional theory (DFT) to complex atomic systems and investigate their mechanical and electronic properties.

At present, we are primarily interested in the following subjects,

1. Materials under extreme conditions

2. Organic crystal polymorphism

3. Materials defects

We are also developing various codes for different purposes,

1. USPEX: a powerful code for structure prediction

2. PyXtal: a Python library for crystal structure generation

3. vasprun-xml: a Python library for parsing vasprun.xml