molsimplify

In this tutorial, we introduce inline ligand decoration. This feature allows us to add small functional groups to existing ligands while we build the structure, enabling the exploration of a large number of simple functionalizations easily and automatically.

In this tutorial, we describe how to use molSimplify to control the placement of molecules, atoms and complexes on surfaces.

In this tutorial, we show how molSimplify can use an artificial neural network (ANN) and geometry-free descriptors to predict metal–liagnd bond lengths.

In this tutorial, we’ll show how to use molSimplify to perform chemical database screening, leveraging calls to OpenBabel together with useful preprocessing and postprocessing tools for seamless integration with the rest of the molSimplify package.

In this tutorial, we’ll show how to use molSimplify’s custom core functionalization feature. This feature allows new functional groups (FGs) or ligands to be attached to user-specified positions in existing structures, and is most useful for studying the effects of ligand functionalization on transition metal complexes.

In this tutorial, we are going to introduce the latest molSimplify feature – a tool that allows the construction of periodic geometries and facilitates adsorbing any type of molecule supported by the basic molSimplify onto a slab in various ways.

In our first tutorial, we’ll briefly discuss how to use molSimplify to generate simple coordination complexes. Our example today is a cobalt porphyrin with an imidazole axial ligand and an empty 6th coordination site.

molSimplify is an open source Python code that enables high-throughput screening in inorganic chemistry. It generates high-quality geometries of transition metal complexes for use as input in electronic structure calculations.