Dimensionality Reduction

AtomicAI provides several dimensionality reduction methods to project high-dimensional descriptor vectors into 2D or 3D spaces for visualisation and clustering.

Available methods

Command	Method
`pca`	Principal Component Analysis
`lpp`	Locality Preserving Projection
`dim_reduction`	Full pipeline (PCA → LPP → TsLPP)
`dim_reduction_mpi`	Parallel version using MPI
`optimize_tslpp_hyperparameters_without_prediction`	TsLPP hyperparameter search (training only)
`optimize_tslpp_hyperparameters_with_prediction`	TsLPP hyperparameter search with test-set prediction
`predict_tslpp`	Apply a trained TsLPP model to new data

Usage

# PCA only
pca

# LPP only
lpp

# Full pipeline
dim_reduction

# Parallel pipeline (requires mpi4py)
mpirun -n 8 dim_reduction_mpi

# Optimise TsLPP hyperparameters then predict
optimize_tslpp_hyperparameters_with_prediction
predict_tslpp

TsLPP

Temperature-scaled Locality Preserving Projection (TsLPP) is a supervised variant of LPP that uses temperature labels to improve the separation of structural phases in the projected space. It is particularly effective for classifying amorphous, liquid, and crystalline phases.