Construction of Potential Energy Surfaces for Complex Energy Materials Systems

This competition track aims to use machine learning methods to construct potential energy surfaces for complex systems in energy materials. Participants are required to accurately capture interactions within complex material systems using advanced artificial intelligence techniques and develop efficient and reliable potential energy models. This process is intended to promote the research and development of new energy materials, providing scientific support and innovative breakthroughs for future energy solutions.

Accurate Vector Mapping of Buildings and Roads Based on Remote Sensing Al Digital Intelligence Technology (For short: RSAIVector)

The objective of this competition is to explore innovations in the field of remote sensing image vector mapping through the application of "intelligent" scientific and technological advancements, thereby promoting the development of intelligent education. Participating teams will use the designated dataset and employ artificial intelligence techniques to accomplish two key tasks: extracting building vector contours and mapping road vectors from remote sensing images. The Open Geospatial Engine (OGE) will serve as the testing platform. It is anticipated that leveraging "intelligent" scientific technologies will provide more effective geoinformatics solutions for practical applications such as autonomous driving and vehicle-road coordination.