Analysis and post-processingΒΆ

This section contains recipes that analyze the output of a simulation, for post-processing, diagnostics or visualization purposes.

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
Local Prediction Rigidity analysis
In this tutorial, we calculate the SOAP descriptors of an amorphous silicon dataset using featomic, then compute the local prediction rigidity (LPR) for the atoms of a "test" set before and after modifications to the "training" dataset has been made.
Local Prediction Rigidity analysis
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
Rotating equivariants
This example shows how to rotate equivariant properties of atomic structures using the scipy and quaternionic libraries. The equivariant properties for this example are computed by the featomic library.
Rotating equivariants
Water orientation in a pulsed electric field
Energy dissipation in water is very fast and more efficient than in many other liquids. This behavior is commonly attributed to the intermolecular interactions associated with hydrogen bonding. This effect has been studied intensively by experiments, ab initio, and classical simulations in the work by Elgabarty et al.. Here, we will re run some of the classical force field molecular dynamics (MD) simulations of the paper using the GROMACS package to compute the timeseries of the dipole moments as well as the energy.
Water orientation in a pulsed electric field
Phonon dispersions with unconstrained models and uncertainty quantification
Phonon dispersion curves are important experimental probes of the lattice dynamics of a material, and are commonly used to validate MLIPs. They are also crucial for computing temperature-dependent properties such as free energies and thermal conductivity.
Phonon dispersions with unconstrained models and uncertainty quantification
Thermal conductivity from the Boltzmann transport equation
This recipe shows how to compute lattice thermal conductivity \kappa by solving the phonon Boltzmann transport equation (BTE) with kALDo and the PET-MAD universal machine-learning potential via the UPET calculator.
Thermal conductivity from the Boltzmann transport equation
NMR-shielding-driven structure determination with ShiftML3
This recipe shows how to use a fast machine-learning surrogate for ab initio NMR chemical-shielding calculations -- ShiftML3 -- to pick out an experimentally observed crystal structure from a pool of candidate polymorphs. In a nutshell, the NMR-shielding-driven structure determination protocol, also sometimes referred to as NMR crystallography or NMR crystal-structure prediction (NMR-CSP), aims to enhance the confidence in a classical crystal structure prediction (CSP) workflow by anchoring the final structure selection step to experimental solid-state NMR data. This method is particularly useful to determine the structure of organic solids, for which it is difficult to obtain single crystals suitable for X-ray diffraction, or to unambiguously determine protonation states who cannot be resolved by XRD.
NMR-shielding-driven structure determination with ShiftML3
Machine-learned dipoles and infrared spectroscopy of liquid water
Machine-learned dipoles and infrared spectroscopy of liquid water