From e2c28d3d46f43cf2ac14da41bda6f2928347dcc3 Mon Sep 17 00:00:00 2001 From: Luca Massimiliano Ghiringhelli <luca@fhi-berlin.mpg.de> Date: Mon, 25 Oct 2021 14:25:32 +0000 Subject: [PATCH] Update metainfo.json --- metainfo.json | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/metainfo.json b/metainfo.json index baccd4a..58d6819 100644 --- a/metainfo.json +++ b/metainfo.json @@ -4,35 +4,34 @@ ], "email": "langer@fhi-berlin.mpg.de", "title": "cmlkit: Toolkit for Machine Learning in Computational Condensed Matter Physics and Quantum Chemistry", - "description": "In this tutorial we will get to know cmlkit, a python package for specifying, evaluating, and optimising machine learning models, and use it to compete in the Nomad 2018 Kaggle challenge.", + "description": "In this tutorial, we will get to know cmlkit, a python package for specifying, evaluating, and optimising machine learning models, and use it to compete in the Nomad 2018 Kaggle challenge.", "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-cmlkit", "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/cmlkit.ipynb", "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/cmlkit.ipynb", "link_paper": "https://arxiv.org/abs/2003.12081", - "updated": "2020-03-26", + "updated": "2021-01-14", "flags":{ "featured": true, "top_of_list": false }, "labels": { - "application_keyword": [ - "Formation energy prediction" - ], "application_section": [ "Tutorials for artificial-intelligence methods" ], "application_system": [ - "Group-III oxides" + "Transparent conducting oxides" ], "category": [ - "intermediate_tutorial" + "advanced_tutorial" ], - "data_analytics_method": [ + "ai_methods": [ + "Supervised learning", + "Regression", "Kernel ridge regression", "SOAP", "MBTR", - "Symmetry Functions" + "Symmetry functions" ], "platform": [ "jupyter" -- GitLab