Our blogs are constructed in three parts: (1) high-level blogs that aim to motivate people with either AI or Science background to learn and join this exciting interdisciplinary field, (2) hands-on blogs on each individual topic (either an AI technique or a scientific problem/discipline), (3) knowledge base that collects the basic explanations of terminologies.

Announcing AI for Science Blogs

  • What we are doing? Why we are doing? Who are we? How do you get involved?

AI for Scientific Discovery

  • Why is AI for Science an exciting field for people with Science backgrounds?

Scientific Discovery in the era of AI

  • Why is AI for Science an exciting field for people with AI backgrounds?

Molecular Simulation

  • Molecular simulation is a computational method used to model and study the behavior of molecules and chemical systems.

Gaussian Processes

  • Gaussian processes are a central model in probabilistic machine learning with particularly favourable properties for uncertainty quantification and Bayesian optimisation.

Causal Machine Learning

  • Causal machine learning aims to identify the causal relationships that govern various observed phenomena is an essential objective across diverse scientific disciplines, as it helps researchers understand the underlying mechanisms and facilitate principled analysis.

Statistical Learning Theory

  • Statistical learning theory provides theoretical tools to analyze deep learning models.


  • Optimization is essential for deep learning to train and update/search model parameters.

Knowledge Base

  • Basic terminologies and short explanations.