molSimplify Tutorials

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: Introduction
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 2: Slab Builder
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 3: Custom Core Functionalization
molSimplify Tutorial 4: Database Searching
In this tutorial, we’ll show how to use molSimplify to perform chemical database screening, leveraging on 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 4: Database Searching
molSimplify Tutorial 5: Using machine learning to build better structures
We have recently implemented an artificial neural network (ANN) in molSimplify – you can read all about it in our recent Chemical Science paper here, but the basic principle is easy to understand.
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 follow up to our first tutorial , we are going to describe how to use molSimplify to control the placement of molecules, atoms and complexes on surfaces. We’ll assume you have followed the last tutorial and have a file containing the xyz coordinates of your surface (you might want to relax the surface geometry in a quantum chemistry code first, depending on your usecase).
Mar 10, 2017 molsimplify-tutorials, tutorials
molSimplify Tutorial 6: Placing structures on slabs
molSimplify Tutorial 7: Easy ligand functionalization in molSimplify
One of the most common ways in which we can think about improving on a given molecule, whether it is an inorganic catalyst or even an organic drug, is to explore small functionalizations of the basic structure – adding small new groups or replacing existing ones while keeping the core intact.
Oct 2, 2017 molsimplify-tutorials, tutorials
molSimplify Tutorial 7: Easy ligand functionalization in molSimplify
molSimplify Tutorial 8: Higher Period Transition Metal Complexes
So far in our molSimplify tutorials, we have been focusing on generating structures for first row transition metal complexes. While the study of such complexes is certainly crucial for research areas such as catalysis and pharmacology, it is imperative that we also include heavy metal complexes in our investigations in order to motivate rapid advancement of inorganic chemistry research.
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. Today’s example involves the following Zn(II) intermediate in a model carbonic anhydrase catalytic cycle, stored as zncat in our Cores subdirectory:
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
molSimplify comes with about 160 built in common ligands and a nifty decoration manager to modify them. However, this cannot hope to address the scope of possible ligands, so we also support providing your own ligands as SMILES or in 3D molecule formats.
May 9, 2018 molsimplify-tutorials, tutorials
molSimplify Tutorial 10: Adding ligands to molSimplify
molSimplify Tutorial 11: Using molSimplify in Python scripts
Many of the classes and methods that are used in molSimplify can be incorporated into your own python scripts. This can be used to extend the geometric manipulation routines in molSimplify to custom use-cases, or to help post-processing the results of DFT calculations.
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
Geometry optimization with density functional theory (DFT), a general procedure to obtain the ground state structures of a complex, is computationally demanding in terms of time and can also easily fail.
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.
With the static classifier, we achieve great performance on job status predictions of out-of-sample data points, capturing their failure before doing the simulation thus saving lots of computational resources (Tutorial 12).
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
Install recommendations: For users who intend to develop (i.e., change the source code) with molSimplify, we recommend an install from source. If you have difficulties installing from source, we recommend Docker or Conda, where we maintain a static image of molSimplify that includes machine learning models that we have published.
Oct 27, 2021 molsimplify-tutorials, tutorials
Installing molSimplify
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