Thailand Machine Learning for Chemistry Competition (TMLCC) 2021 is a collaborative project between King Mongkut’s University of Technology Thonburi (KMUTT) and Computational Science and Engineering Association (CSEA), Thailand. The aim of the TMLCC 2021 is to encourage Thai students, researchers, and regular people to aware the importance of mahine learning (ML) in chemistry and materials science.

The challenge of this year is to predict the carbon dioxide working capacity of metal-organic frameworks (MOFs).


TMLCC 2021 poster


  • You need to upload a Youtube video as part of your submission (max. 5 minutes). The video should describe the models you developed, the techniques you used, the analysis you did, the prediction you made, and the results you got.
  • Write a description of the project, its features, all of the tools or libraries you used, and how it was built. Feel free to share your creative ideas!
  • Provide a URL to your open source Deepnote project or GitHub code repository. The project or repository must be public, contain all source code files, dependencies, including the file types specific to the component category, and have an open-source license such as the following:
  • Please do assign every team member to the corresponding team.
  • Submit all the information and material of your prototype to Devpost.
  • The main committee and the representative of the sponsors will assess your projects on Devpost. The best team will get the creativity prize.
  • Wait for judging and results.

Hackathon Sponsors


$4,329 in prizes

The Winner

Selected from the Leaderboard of phase 3

1st Runner Up (2)

Selected from the Leaderboard of phase 3

2nd Runner Up (2)

Selected from the Leaderboard of phase 3


For the team that uses or applies the creative idea or wonderful machine learning techniques.

Outstanding Submission (3)

For selected three outstanding projects

Popular Vote

For the team that gets the most votes.

Deepnote Pro Plan Subscription

- 1 Prize for the Winner team

Devpost Achievements

Submitting to this hackathon could earn you:


Asst. Prof. Sukree Sinthupinyo -

Asst. Prof. Sukree Sinthupinyo -
Chulalongkorn University

Asst. Prof. Nongnuch Artrith

Asst. Prof. Nongnuch Artrith
Utrecht University

Dr. Unchalisa Taetragool

Dr. Unchalisa Taetragool

Asst. Prof. Sila Kittiwachana

Asst. Prof. Sila Kittiwachana
Chiang Mai University

Dr. Suttipong Wannapaiboon

Dr. Suttipong Wannapaiboon
Synchrotron Light Research Institute

Judging Criteria

  • Creativity and Uniqueness (25%)
    How creative or innovative is the idea behind the model? Is the idea being unlike anything else or being solitary in the use of new descriptors or machine learning techniques.
  • Implementation of the idea (25%)
    Includes how well the idea was executed - is the code working? is the demo working?
  • Quality of Presentation (25%)
    Was the team able to explain their idea and what the machine learning actually did? Did the presentation show the essence of their model and has fulfilled the judging criteria?
  • Submission Quality (25%)
    The quality of the TMLCC project delivered based on the write up, the video, the code, and the degree to which the solution could impact individuals and/or businesses.

Questions? Email the hackathon manager

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