Artificial Intelligence (AI) has been revolutionizing numerous fields within science and engineering, giving rise to “AI for Science” – a new paradigm for both academia and industry. In this seminar, we will explore how AI algorithms and tools empower researchers to tackle intricate scientific challenges, strengthen collaboration, and expedite the discovery process. We will also envision the future of this field in the era of cloud-native infrastructures and large language models, emphasizing the crucial role of platform engineering in nurturing the growth and development of the entire scientific community. Lastly, we will provide examples to illustrate the potential appearance of such platforms and discuss the path towards their realization.
Dr Linfeng Zhang, a researcher at the AI for Science Institute in Beijing and founder of DP Technology, holds a background in applied mathematics from Princeton University and physics from Peking University. His work concentrates on the interdisciplinary field of AI for Science, contributing to machine learning, computational physics and chemistry, and materials and drug design. Linfeng is the major developer of DeePMD-kit, an open-source software for molecular simulation, and has been promoting the DeepModeling community for AI for Science enthusiasts. His efforts have led to several significant projects and recognition, including the ACM Gordon Bell Prize in 2020, and a feature on the cover of Forbes Asia’s 30 Under 30 list for 2022.
Clean water and clean air are vital for public health. This project focuses on developing high-efficiency and environmentally sustainable filters for removing harmful air/water pollutants. The team has developed novel architectures and functionalities for the filters to achieve high permeance, high removal efficiency, and excellent reusability.
