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Posters

all students are encouraged to bring posters (max A0 portrait)

poster prize winners

  • Simone Vari, Politecnico di Milano, Italy
  • Sophie Bennett, University of Southampton, UK
  • Gustavo Chaparro, Imperial College London, UK

poster

Machine Learning for Chemistry Blog Tutorial Contest

We are excited to announce a Machine Learning for Chemistry Blog Tutorial Contest for PhD students attending the school on ML for Chemistry! This is your chance to apply what you've learned, work in teams, and create an engaging tutorial for a wider audience of scientists interested in machine learning.

🏆 The Challenge

Each team (randomly assigned groups of four) will write a Medium-style blog tutorial on an aspect of the course material. Your target audience is scientists who are Python-literate chemists, but not necessarily ML experts. The best tutorials will be published online and receive special recognition!

📌 Guidelines

  • Choose a topic based on the course material (e.g., equivariant machine learning potentials, generative models for materials discovery, graph neural networks).
  • Explain concepts in a clear, intuitive, and engaging way.
  • Include code snippets, figures, and real-world applications where appropriate.
  • The expected length is 1,500–2,500 words.
  • Work collaboratively—teams should divide writing, coding, and editing tasks effectively.
  • All content should be original and cite relevant sources where applicable.

📅 Submission Details

  • Deadline: April 25th, 16:00 BST

  • Format:

  • Written tutorial in Markdown (with embedded figures).
  • Accompanying code in a Jupyter Notebook (if applicable).
  • License: All accepted tutorials will be published on the CaMMLS website under a CC-BY license.

🏅 Prizes & Recognition

  • Winning team receives:
  • £200 prize (to be shared).
  • Four ML textbooks (to be shared).
  • All winning and high-quality entries will be published on the CaMMLS website.
  • Winners will be featured in the AIChemy newsletter.

🔍 Evaluation Criteria

Submissions will be judged on:

  • Clarity & accessibility – Is the tutorial easy to follow for the target audience?
  • Technical correctness – Are explanations and code correct and well-integrated?
  • Engagement & writing style – Is the tutorial enjoyable to read?
  • Use of visuals & code – Are figures and code snippets effective?
  • Creativity & originality – Does the tutorial bring a fresh perspective?

📢 Sponsorship

This contest is generously sponsored by npj Computational Materials and AIChemy.