List of all recipes =================== This section contains the list of all compiled recipes, including those that are not part of any of the other sections. .. grid:: 1 2 2 3 :gutter: 1 1 2 3 .. grid-item:: .. card:: Batch run of CP2K calculations :link: examples/batch-cp2k/reference-trajectory :link-type: doc :text-align: center :shadow: md .. image:: examples/batch-cp2k/images/thumb/sphx_glr_reference-trajectory_thumb.png :alt: This is an example how to perform single point calculations based on list of structures using CP2K using its reftraj functionality. The inputs are a set of structures in :download:`example.xyz` using the DFT parameters defined in :download:`reftraj_template.cp2k`. The reference DFT parameters are taken from Cheng et al. Ab initio thermodynamics of liquid and solid water 2019. Due to the small size of the test structure and convergence issues, we have decreased the size of the CUTOFF_RADIUS from 6.0\,\mathrm{Å} to 3.0\,\mathrm{Å}. For actual production calculations adapt the template! :class: gallery-img .. grid-item:: .. card:: Generalized Convex Hull construction for the polymorphs of ROY :link: examples/roy-gch/roy-gch :link-type: doc :text-align: center :shadow: md .. image:: examples/roy-gch/images/thumb/sphx_glr_roy-gch_thumb.png :alt: 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 rascaline and uses the directional convex hull function from scikit-matter to make the figure. :class: gallery-img .. grid-item:: .. card:: LODE Tutorial :link: examples/lode-linear/lode-linear :link-type: doc :text-align: center :shadow: md .. image:: examples/lode-linear/images/thumb/sphx_glr_lode-linear_thumb.png :alt: This tutorial explains how Long range equivariant descriptors can be constructed using rascaline and the resulting descriptors be used to construct a linear model with equisolve :class: gallery-img .. grid-item:: .. card:: LPR analysis for amorphous silicon dataset :link: examples/lpr/lpr :link-type: doc :text-align: center :shadow: md .. image:: examples/lpr/images/thumb/sphx_glr_lpr_thumb.png :alt: In this tutorial, we calculate the SOAP descriptors of an amorphous silicon dataset using rascaline, 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. :class: gallery-img .. grid-item:: .. card:: PCA/PCovR Visualization for the rattled GaAs training dataset :link: examples/gaas-map/gaas-map :link-type: doc :text-align: center :shadow: md .. image:: examples/gaas-map/images/thumb/sphx_glr_gaas-map_thumb.png :alt: This example uses rascaline and metatensor to compute structural properties for the structures in a training for a ML model. 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. :class: gallery-img .. grid-item:: .. card:: Path integral metadynamics :link: examples/pi-metad/pi-metad :link-type: doc :text-align: center :shadow: md .. image:: examples/pi-metad/images/thumb/sphx_glr_pi-metad_thumb.png :alt: 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. :class: gallery-img .. grid-item:: .. card:: Path integral molecular dynamics :link: examples/path-integrals/path-integrals :link-type: doc :text-align: center :shadow: md .. image:: examples/path-integrals/images/thumb/sphx_glr_path-integrals_thumb.png :alt: 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. :class: gallery-img .. grid-item:: .. card:: Periodic Hamiltonian learning :link: examples/periodic-hamiltonian/periodic-hamiltonian :link-type: doc :text-align: center :shadow: md .. image:: examples/periodic-hamiltonian/images/thumb/sphx_glr_periodic-hamiltonian_thumb.png :alt: This tutorial explains how to train a machine learning model for the electronic Hamiltonian of a periodic system. Even though we focus on periodic systems, the code and techniques presented here can be directly transferred to molecules. :class: gallery-img .. grid-item:: .. card:: Quantum heat capacity of water :link: examples/heat-capacity/heat-capacity :link-type: doc :text-align: center :shadow: md .. image:: examples/heat-capacity/images/thumb/sphx_glr_heat-capacity_thumb.png :alt: This example shows how to estimate the heat capacity of liquid water from a path integral molecular dynamics simulation. The dynamics are run with i-PI, and LAMMPS is used as the driver to simulate the q-TIP4P/f water model. :class: gallery-img .. grid-item:: .. card:: Sample and Feature Selection with FPS and CUR :link: examples/sample-selection/sample-selection :link-type: doc :text-align: center :shadow: md .. image:: examples/sample-selection/images/thumb/sphx_glr_sample-selection_thumb.png :alt: In this tutorial we generate descriptors using rascaline, then select a subset of structures using both the farthest-point sampling (FPS) and CUR algorithms implemented in scikit-matter. Finally, we also generate a selection of the most important features using the same techniques. :class: gallery-img .. grid-item:: .. card:: Training the DOS with different Energy References :link: examples/dos-align/dos-align :link-type: doc :text-align: center :shadow: md .. image:: examples/dos-align/images/thumb/sphx_glr_dos-align_thumb.png :alt: This tutorial would go through the entire machine learning framework for the electronic density of states (DOS). It will cover the construction of the DOS and SOAP descriptors from ase Atoms and eigenenergy results. A simple neural network will then be constructed and the model parameters, along with the energy reference will be optimized during training. A total of three energy reference will be used, the average Hartree potential, the Fermi level, and an optimized energy reference starting from the Fermi level energy reference. The performance of each model is then compared. :class: gallery-img .. toctree:: :maxdepth: 1 :hidden: :titlesonly: examples/batch-cp2k/reference-trajectory examples/roy-gch/roy-gch examples/lode-linear/lode-linear examples/lpr/lpr examples/gaas-map/gaas-map examples/pi-metad/pi-metad examples/path-integrals/path-integrals examples/periodic-hamiltonian/periodic-hamiltonian examples/heat-capacity/heat-capacity examples/sample-selection/sample-selection examples/dos-align/dos-align