Tutorials

We developed the tensioned model for bond activation (TMBA), a multivariate linear regression model trained on experimentally studied covalent mechanophores that can accurately predict transition forces in polymer mechanochemistry. In this tutorial, we will show how to calculate the transition force ($f^*$) using TMBA.

molSimplify can be used to visualize molecules built from SMILES strings. In this tutorial, we will use molSimplify to generate several variants of a molecule and display them in a grid.

This tutorial describes installation options for molSimplify, including from Conda and building from source.

In this tutorial, we demonstrate job control system that can offer an effective two-fold acceleration for your calculations by killing simulations with a low expected success rate based on a dynamic classifier machine learning model.

In this tutorial, we demonstrate the use of a machine learning classification model that predicts whether a geometry optimization will be successful.

In this tutorial, we show how you can use the classes and methods of molSimplify to build your own custom analysis or molecule generation scripts.

In this tutorial, we demonstrate how you can use your own ligands in molSimplify by providing either SMILES strings or using 3D molecule formats.

In this two-part tutorial, we’ll show you how to use molSimplify to collect autocorrelation-based descriptors from molecular structures and use those to make predictions using a simple kernel ridge regression (KRR) model, as shown in our recent paper.

In the second part of our tutorial, we will demonstrate how to use R to conduct kernel based prediction of atomization energies based on RACs. You’ll need QM9_descriptor_file.csv, which we prepared using molSimplify in the previous tutorial and also provide here.

In this tutorial, we will show how to replace multidentate ligands in a prebuilt complex. This feature, which facilitates screening of diverse catalyst scaffolds, was recently added to molSimplify.