{"cells":[{"metadata":{},"cell_type":"markdown","source":"**Local Many-body Tensor Representation**\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install dscribe","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install --upgrade --user ase","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.\nstructures = pd.read_csv(f'../input/structures.csv')","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"from dscribe.descriptors import LMBTR\nfrom ase import Atoms","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"lmbtr = LMBTR(\n    species=[\"C\", \"H\", \"N\"],\n    k2 = {\n        \"geometry\": {\"function\": \"inverse_distance\"},\n        \"grid\": {\"min\": 0.1, \"max\": 2, \"sigma\": 0.1, \"n\": 10},\n        \"weighting\": {\"function\": \"exp\", \"scale\": 0.75, \"cutoff\": 1e-2}\n    },\n    k3 = {\n        \"geometry\": {\"function\": \"angle\"},\n        \"grid\": {\"min\": 0, \"max\": 180, \"n\": 10, \"sigma\": 2},\n        \"weighting\": {\"function\": \"exp\", \"scale\": 0.5, \"cutoff\": 1e-3},\n    },\n    periodic=False,\n    normalization=\"l2_each\",\n)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Randomly select a molecule\nm1 = structures.loc[structures['molecule_name']=='dsgdb9nsd_133884']\na1 = Atoms(''.join(m1['atom'].values), positions=m1[['x','y','z']].values)\ndisplay(a1)\ndisplay(m1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Create LMBTR output for the system\nlmbtr_a1 = lmbtr.create(a1, positions=[0,1,2])\n\nprint(lmbtr_a1)\nprint(lmbtr_a1.shape)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}