chemiscope

Chemiscope is a tool for interactive exploration of databases of materials and molecules, correlating local and global structural representations with the properties of the systems. Chemiscope files can be viewed online, generated using a python library and used inside jupyter notebooks or sphinx documentations, which is how it’s used in the cookbook examples. You can check the documentation and the github repository.

Generalized Convex Hull construction for the polymorphs of ROY
This notebook analyzes the structures of 264 polymorphs of ROY, from Beran et Al, Chemical Science (2022), comparing the conventional density-energy convex hull with a Generalized Convex Hull (GCH) analysis (see Anelli et al., Phys. Rev. Materials (2018)). It uses features computed with featomic and uses the directional convex hull function from scikit-matter to make the figure.
Generalized Convex Hull construction for the polymorphs of ROY
PCA/PCovR Visualization of a training dataset for a potential
This example uses featomic and metatensor to compute structural properties for the structures in a training dataset for a ML potential. These are then used with simple dimensionality reduction algorithms (implemented in sklearn and skmatter) to obtain a simplified description of the dataset, that is then visualized using chemiscope.
PCA/PCovR Visualization of a training dataset for a potential
Path integral metadynamics
This example shows how to run a free-energy sampling calculation that combines path integral molecular dynamics to model nuclear quantum effects and metadynamics to accelerate sampling of the high-free-energy regions.
Path integral metadynamics
Path integral molecular dynamics
This example shows how to run a path integral molecular dynamics simulation using i-PI, analyze the output and visualize the trajectory in chemiscope. It uses LAMMPS as the driver to simulate the q-TIP4P/f water model.
Path integral molecular dynamics
Constant-temperature MD and thermostats
This recipe gives a practical introduction to finite-temperature molecular dynamics simulations, and provides a guide to choose the most appropriate thermostat for the simulation at hand.
Constant-temperature MD and thermostats
Multiple time stepping and ring-polymer contraction
This notebook provides an introduction to multiple time stepping and ring polymer contraction, two closely-related techniques, that are geared towards reducing the cost of calculations by separating slowly-varying (and computationally-expensive) components of the potential energy from the fast-varying (and hopefully cheaper) ones.
Multiple time stepping and ring-polymer contraction
Atomistic Water Model for Molecular Dynamics
In this example, we demonstrate how to construct a metatomic model for flexible three and four-point water model, with parameters optimized for use together with quantum-nuclear-effects-aware path integral simulations (cf. Habershon et al., JCP (2009)). The model also demonstrates the use of torch-pme, a Torch library for long-range interactions, and uses the resulting model to perform demonstrative molecular dynamics simulations.
Atomistic Water Model for Molecular Dynamics
Equivariant linear model for polarizability
In this example, we demonstrate how to construct a metatensor atomistic model for the polarizability tensor of molecular systems. This example uses the featomic library to compute equivariant descriptors, and scikit-learn to train a linear regression model. The model can then be used in an ASE calculator. You could also have a look at this recipe based on scalar/tensorial models, which provides an alternative approach for equivariant learning of tensors.
Equivariant linear model for polarizability
Equivariant model for tensorial properties based on scalar features
In this example, we demonstrate how to train a metatensor atomistic model on dipole moments and polarizabilities of small molecular systems, using a model that combines scalar descriptors with equivariant tensorial components that depend in a simple way from the molecular geometry. You may also want to read this recipe for a linear polarizability model, which provides an alternative approach for tensorial learning. The model is trained with metatrain and can then be used in an ASE calculator.
Equivariant model for tensorial properties based on scalar features
The PET-MAD universal potential
This example demonstrates how to use the PET-MAD model with ASE, i-PI and LAMMPS. PET-MAD is a "universal" machine-learning forcefield trained on a dataset that aims to incorporate a very high degree of structural diversity.
The PET-MAD universal potential
Long-stride trajectories with a universal FlashMD model
For a quickstart on how to use FlashMD with ASE, you can go here.
Long-stride trajectories with a universal FlashMD model
ML collective variables in PLUMED with metatomic
This example shows how to build a metatomic model that computes order parameters for a Lennard-Jones cluster, and how to use it with the PLUMED package to run a metadynamics simulation.
ML collective variables in PLUMED with metatomic