An automated solution for carbon monoxide functionalization which combinesmachine learning descriptors with automated software control of the tip preparation process.
The machine learning models are implemented in Tensorflow 1.12. The code is currently written in Python 3. At least the following Python packages are required:
- numpy
- matplotlib
- tensorflow-gpu=1.12.0
- jupyter
Additionally, you need to have Cuda and cuDNN correctly configured on your system in order to train the models on an Nvidia GPU.
AFM-data with CO-tips samples can be downloaded here.
If you are using Anaconda, you can create the required Python environment with
conda env create -f environment.ymlThis will create a conda enviroment named tf-gpu with the all the required packages. It also has a suitable version of the Cuda toolkit and cuDNN already installed. Activate the environment with
conda activate py3-tf12To create the datasets and train the models, run jupyter notebook in the repository folder, open the train_TF.ipynb notebook, and follow the instructions therein.
The folder pretrained_weights holds the weights for pretrained model.
To predict quality of CO-tip on some set of images of CO tips, open the predict_TF.ipynb notebook, and follow the instructions therein. Good CO tips predicted as 1, bads COs as 0.
To perform autonomous CO functionalization, open the auto-co.ipynb notebook, and follow the instructions therein. Ensure that your CreaTec STM is already connected and that COM support is enabled during CreaTec STMAFM software installation.