{
  "id": 174671,
  "title": "Competition publication",
  "url": "/competitions/champs-scalar-coupling/discussion/174671",
  "author_name": "",
  "post_date": "2020-08-14T15:08:45.473691900Z",
  "votes": 4,
  "comment_count": 2,
  "views": 0,
  "content": "<p>Hi everyone.</p>\n<p>We have submitted a work on the competition for publication. It's openly available here: <a href=\"https://arxiv.org/abs/2008.05994\" target=\"_blank\">https://arxiv.org/abs/2008.05994</a>. Thanks to all of you for participating!</p>",
  "messages": [
    {
      "id": "970566",
      "postDate": "08/14/2020 15:08:45",
      "content": "<p>Hi everyone.</p>\n<p>We have submitted a work on the competition for publication. It's openly available here: <a href=\"https://arxiv.org/abs/2008.05994\" target=\"_blank\">https://arxiv.org/abs/2008.05994</a>. Thanks to all of you for participating!</p>",
      "rawMarkdown": "Hi everyone.\n\nWe have submitted a work on the competition for publication. It's openly available here: https://arxiv.org/abs/2008.05994. Thanks to all of you for participating!",
      "votes": null
    },
    {
      "id": "970806",
      "postDate": "08/14/2020 20:13:43",
      "content": "<p>Great paper, thanks for the write up. Lots of clear, yet surprising, take-aways for those wishing to apply ML to other physical property predictions. The main one for me was this challenge didn't need domain expertise as much as I initially thought it would.</p>",
      "rawMarkdown": "Great paper, thanks for the write up. Lots of clear, yet surprising, take-aways for those wishing to apply ML to other physical property predictions. The main one for me was this challenge didn't need domain expertise as much as I initially thought it would.",
      "votes": null
    },
    {
      "id": "1103582",
      "postDate": "12/06/2020 03:45:40",
      "content": "<p>Great work， I have been working on this competition for months to explore methods in deep chemistry.  This publication  will definetely help me in my research.</p>",
      "rawMarkdown": "Great work， I have been working on this competition for months to explore methods in deep chemistry.  This publication  will definetely help me in my research.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 970806,
      "author_name": "phucco",
      "author_url": "",
      "post_date": "08/14/2020 20:13:43",
      "content": "<p>Great paper, thanks for the write up. Lots of clear, yet surprising, take-aways for those wishing to apply ML to other physical property predictions. The main one for me was this challenge didn't need domain expertise as much as I initially thought it would.</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 1103582,
      "author_name": "zhaowendao",
      "author_url": "",
      "post_date": "12/06/2020 03:45:40",
      "content": "<p>Great work， I have been working on this competition for months to explore methods in deep chemistry.  This publication  will definetely help me in my research.</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "970566": "Hi everyone.\n\nWe have submitted a work on the competition for publication. It's openly available here: https://arxiv.org/abs/2008.05994. Thanks to all of you for participating!",
    "970806": "Great paper, thanks for the write up. Lots of clear, yet surprising, take-aways for those wishing to apply ML to other physical property predictions. The main one for me was this challenge didn't need domain expertise as much as I initially thought it would.",
    "1103582": "Great work， I have been working on this competition for months to explore methods in deep chemistry.  This publication  will definetely help me in my research."
  },
  "source": "meta"
}