Descriptor

Select structures based on descriptors.

Two sparsification methods are supported.

• cur:

  Run CUR decomposition to select the most representative structures. This method computes a CUR score for every structure and strategy defines the selection either performs a deterministic selection (descent), structures with the number largest scores, or a random one (stochastic), structures with higher scores that have higher probability. If zeta is larger than 0., the input descripters will be transformed as MATMUL(descriptors.T, descriptors)^zeta.

• fps:

  The farthest point sampling strategy. min_distance can be set to adjust the sparsity of selected structures in the feature (descriptor) space.

selection:
    - method: descriptor
    descriptor:
        name: soap
        species: ["H", "O", "Pt"]
        rcut : 6.0
        nmax : 12
        lmax : 8
        sigma : 0.3
        average : inner
        periodic : true
    sparsify:
        method: cur # fps
        zeta: -1
        strategy: descent
    number: [16, 1.0]

This selection will produce a picture to visualise the distribution of structures.

Note

This requires the python package dscribe to be installed. Use pip install or conda install dscribe -c conda-forge.