Kulik Research Group

molSimplify: Introduction

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.

Apr 29, 2016 molsimplify-tutorials, tutorials

molSimplify Tutorial 1: Structure Generation

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.

Jun 18, 2016 molsimplify-tutorials, tutorials

molSimplify Tutorial 1: Structure Generation

molSimplify Tutorial 2: Slab Builder

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.

Dec 2, 2016 molsimplify-tutorials, tutorials

molSimplify Tutorial 3: Custom Core Functionalization

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.

Dec 25, 2016 molsimplify-tutorials, tutorials

molSimplify Tutorial 4: Database Searching

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.

Dec 25, 2016 molsimplify-tutorials, tutorials

molSimplify Tutorial 5: Using machine learning to build better structures

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

Feb 27, 2017 molsimplify-tutorials, tutorials

molSimplify Tutorial 5: Using machine learning to build better structures

molSimplify Tutorial 6: Placing structures on slabs

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

Mar 10, 2017 molsimplify-tutorials, tutorials

molSimplify Tutorial 6: Placing structures on slabs

molSimplify Tutorial 7: Easy ligand functionalization in 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.

Oct 2, 2017 molsimplify-tutorials, tutorials

molSimplify Tutorial 7: Easy ligand functionalization in molSimplify

molSimplify Tutorial 8: Higher Period Transition Metal Complexes

In this tutorial, we will show how to use molSimplify for generating structures of second and third row transition metal complexes.

Dec 6, 2017 molsimplify-tutorials, tutorials

molSimplify Tutorial 8: Higher Period Transition Metal Complexes

molSimplify Tutorial 9: Bidentate Ligand Replacement

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.

Dec 21, 2017 molsimplify-tutorials, tutorials

QM9 kernel models using molSimplify, RACs and R: Part 2

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.

Feb 20, 2018 molsimplify-tutorials, tutorials

QM9 kernel models using molSimplify, RACs and R: Part 2

QM9 kernel models using molSimplify, RACs and R: Part 1

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.

Feb 20, 2018 molsimplify-tutorials, tutorials

QM9 kernel models using molSimplify, RACs and R: Part 1

molSimplify Tutorial 10: Adding ligands to molSimplify

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

May 9, 2018 molsimplify-tutorials, tutorials

molSimplify Tutorial 10: Adding ligands to molSimplify

molSimplify Tutorial 11: Using molSimplify in Python scripts

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.

Feb 5, 2019 molsimplify-tutorials, tutorials

molSimplify Tutorial 11: Using molSimplify in Python scripts

molSimplify Tutorial 12: Using the static classifier to predict your simulation outcomes before they waste your time

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

Mar 25, 2019 molsimplify-tutorials, tutorials

molSimplify Tutorial 12: Using the static classifier to predict your simulation outcomes before they waste your time

molSimplify Tutorial 13: molscontrol -- an intelligent job control system to manage your DFT geometry optimizations for inorganic discovery.

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.

Apr 1, 2019 molsimplify-tutorials, tutorials

molSimplify Tutorial 13: molscontrol -- an intelligent job control system to manage your DFT geometry optimizations for inorganic discovery.

Installing molSimplify

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

Oct 27, 2021 molsimplify-tutorials, tutorials

Visualizing molecules built from SMILES strings in Jupyter Notebooks using molSimplify

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.

Nov 3, 2021 molsimplify-tutorials, tutorials

Visualizing molecules built from SMILES strings in Jupyter Notebooks using molSimplify

molSimplify Tutorials