2026 Summer Workshop on
Chemical Reaction Informatics

August 31 (Mon) to September 4 (Fri), 2026

Institute for Chemical Reaction Design and Discovery (ICReDD), Hokkaido University

Overview

This workshop brings together researchers in quantum chemistry, chemoinformatics, and machine learning. The goal is to identify high-impact opportunities where machine learning can address central challenges in the design and discovery of chemical reactions.

Rather than following a conventional conference-style presentation format, the meeting will focus on learning fundamental concepts, intensive exchange and discussion of ongoing research, collaborative exploration and refinement of seed research ideas, hands-on experimentation, and initial prototyping in a hackathon format.

Schedule

DayDateTimeProgram
Day 1Aug 31
(Mon)		10:00 to 18:00
  • Welcome and workshop overview (Taki / Buchi)
  • Participant introductions (1 to 3 min each)
  • Background tutorials and interactive Q&A
    • Quantum Chemistry (Buchi)
    • Computational Catalysis (Gao)
    • NNP / AFIR (Ruben)
  • Discussions of related ongoing work
    • AFIR Overview (Buchi)
    • Bio Diffusion (Kai)
    • TS Diffusion (Trond)
  • Welcome gathering
Day 2Sep 1
(Tue)		10:00 to 15:00
  • ML-based Control / Search / Generation of AFs (Taki)
  • GRRM Tutorial (Buchi)
    • GRRM / AFIR
    • SC-AFIR
    • DQN-AFIR
    • ChemOnto-AFIR
  • ASE Interface (Trond)
  • Social gathering
Day 3Sep 2
(Wed)		10:00 to 18:00 Hackathon: Prototype Development
Initial exploration of ML models for AFs
  • Team formation and project planning
  • Brainstorming research directions
  • Quick survey of relevant ML techniques
  • Iterative prototyping and discussions
  • Intermediate progress sharing
Day 4Sep 3
(Thu)		10:00 to 18:00 Hackathon: Prototype Development
Initial exploration of ML models for AFs
  • Short morning check-in
  • Iterative prototyping
  • Technical discussions and mentoring
  • Intermediate progress sharing
Day 5Sep 4
(Fri)		10:00 to 18:00 Hackathon: Prototype Development
Initial exploration of ML models for AFs
  • Short morning check-in
  • Final implementation and debugging
  • Preparation of presentations
  • Team presentations and discussion
  • Feedback and future research directions
  • Wrap-up

Participants

  • Yu Harabuchi (Harabuchi Group, ICReDD)
  • Ruben Staub (Varnek Group, ICReDD)
  • Trond Linjordet (Varnek Group, ICReDD)
  • Xingyu Tang (Varnek Group, ICReDD)
  • Min Gao (Gao Group, ICReDD)
  • Kai Oshiro (Gao Group, ICReDD)
  • Martin (Takigawa Group, ICReDD)
  • Tomoyuki Unno (Takigawa Group, UTokyo)
  • Kentaro Nishioka (Takigawa Group, UTokyo)
  • Xuan Liu (Takigawa Group, UTokyo)
  • Jiali Ma (Takigawa Group, Kyoto Univ)
  • Kishan Wimalawarne (Takigawa Group Guest, Kyushu Univ)
  • Ichigaku Takigawa (Takigawa Group, ICReDD/UTokyo/RIKEN)

Organizers

  • Yu Harabuchi (Harabuchi Group, ICReDD)
  • Ruben Staub (Varnek Group, ICReDD)
  • Min Gao (Gao Group, ICReDD)
  • Ichigaku Takigawa (Takigawa group, ICReDD/UTokyo/RIKEN)

Venue

Institute for Chemical Reaction Design and Discovery (ICReDD), Hokkaido University

Address: Nishi 10 Chome Kita 21 Jo, Kita Ward, Sapporo, Hokkaido

Support

This workshop is supported by the JSPS KAKENHI Grant-in-Aid for Scientific Research (B), “Geometric Graph Representation Learning for Chemistry”, Grant Number 25K03174 (FY2025 to FY2029).