The Effect of Chemical Representation on Active Machine Learning Towards Closed-Loop Optimization



Multivariate chemical reaction optimization involving catalytic systems is a non-trivial task due to the high number of tuneable parameters and discrete choices. Closed-loop optimization featuring active Machine Learning (ML) represents a powerful strategy for automating reaction optimization. However, the translation of chemical reaction conditions into a machine-readable format comes with the challenge of finding highly informative features which accurately capture the factors for reaction success and allow the model to learn efficiently. Herein, we compare the efficacy of different calculated chemical descriptors for a high throughput generated dataset to determine the impact on a supervised ML model when predicting reaction yield. Then, the effect of featurization and size of the initial dataset within a closed-loop reaction optimization was examined. Finally, the balance between descriptor complexity and dataset size was considered. Ultimately, tailored descriptors did not outperform simple generic representations, however, a larger initial dataset accelerated reaction optimization.

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Supplementary material

The Effect of Chemical Representation on Supervised and Active Machine Learning Towards Yield Prediction
Table of Contents General Considerations Analytical Methods High Throughput Experimentation Reaction Scheme and Ligand Structures Synthesis of Materials Preparation of the Dataset Generation of Morgan Fingerprints Density Functional Theory (DFT)-based Geometry Optimization Sterimol Parameters Percentage Buried Volume Natural Bond Orbital (NBO) Analysis CHarges from ELectrostatic Potentials Using a Grid-Based Method (ChELPG) Analysis Summary of DFT Descriptor Values Machine Learning Linear Model Random Forest Gaussian Process Artificial Neural Network Adaptive Boosting Model Support Vector Regression Leave-one-group-out (LOGO) Cross Validation (CV) Feature Importance Assessment of the Random Forest Closed-loop Optimization Expected Improvement Acquisition Function De-full Factorization of the Chemical Space Study Batch-Sequential Active Learning The Impact of Initialization of the Active Learning The Impact of Initialization: Dataset Size vs. Complexity of Parameterization Active Learning Trajectories – Insights