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Dependencies.zip
Example_files.zip
LICENSE.txt
LabMate-AI.py
README.txt
random_reaction_gen.py

README.txt

LabMate.AI was designed to help identifying optimized conditions for chemical reactions.

The software is written in the Python 2.7 programming language and uses the following dependencies, which should be installed:

- NumPy (>= 1.11.3)
- Pandas (>= 0.19.2)
- Scikit-learn (>= 0.18.1)





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random_reaction_gen.py

This script creates two text files that are required to run LabMate.AI:

1) "all_combos.txt" - the file contains all possible combinations of reaction conditions.
2) "train_data.txt" - the file contains 10 random reaction sampled from all possible combinations.


The script requires minor editing and instructions are available once the script is opened with a text editor.
To run the script, open a terminal in your destiny folder and type:

>>> python random_reaction_gen.py


After performing the reactions, add a column in the end of the "train_data.txt" file mentioning the reaction yield/conversion or similar (sample file available)

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LabMate.AI.py


This script implements a routine to search for the next best experiment to be carried out.


To run LabMate.AI, open a terminal and type:

>>> python LabMate-AI.py

in the folder where the Python script, the "train_data.txt" and the "all_combos.txt" files are located. The columns in the txt files must be tab separated.
The grid search routine will run in 6 CPUs (n_jobs=6 as in line 56), so make sure there are enough computational resources available.



Train data txt file:
- Fist column is the reaction identifier
- Last column is the reaction yield/conversion
- Columns in the middle correspond to the descriptor set
- File must be named "train_data.txt", otherwise it will not be recognised by the script.


Search space txt file:
- Fist column is the reaction identifier 
- Following columns correspond to the descriptor set
- File must be named "all_combos.txt", otherwise it will not be recognised by the script.


Output files:
- "best_score.txt" - saves the negative mean absolute error value (lower absolute value is better)
- "feature_importances.txt" - importance (given in the range of 0-1) for each descriptor, according to the random forest algorithm
- "selected_reaction.txt" - this is the next best experiment, as suggested by LabMate.AI
- "predictions.txt" - predictions for all possible reactions
- "random_forest_model_grid.sav" - saves the model