Using Data Science Tools to Explore Rate Matching in a Nickel-Catalyzed Cross-Electrophile Coupling of Alkyl and Aryl Halides (Cl, Br) with a Tridentate Monoanionic Ligand

This repository contains notebooks used in this work, including:

• Correction factors to predict aryl and alkyl chloride descriptor libraries
• Random forest regression models to predict relative rate constants for aryl and alkyl halides
• Visualizations of correlations between rate and yield

The attached Excel sheets include input features for modeling, construction of UMAP, calculated descriptor libraries, and correction factor information.

DFT optimized xyz coordinates are available in xyz_coordinates.

Installation

Two environment files are provided:

• CF_alkyl_rf.yml — for correction factor calculations, random forest alkyl rate modeling

• aryl_rf_env.yml — for random forest aryl rate modeling

• rate_yield_analysis.yml — for rate/yield analysis

  To create and activate an environment:

conda env create -f CF_alkyl_ratematch.yml
conda activate CF_alkyl_ratematch

Contents

Folder	Description
RF_alkyl_rates/	Random forest model for alkyl halide rate prediction
RF_aryl_rates/	Random forest model for aryl halide rate prediction
correction_factor/	Descriptor library correction factors
rate_yield_analysis/	Rate-yield correlation analysis and interactive plots which show structures
xyz_coordinates/	.xyz files for DFT optimized structures

Excel File	Description
molecular_descriptors.xlsx	Calculated / predicted descriptors for aryl and alkyl virtual libraries
correction_factor_info_aryl_alkyl.xlsx	Correction factor training sets and informations
modeling_input_features.xlsx	Input features used to construct models
umap.xlsx	UMAP coordinates and features used to make UMAPs