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Machine Learning facilitates building a large variety of models, startingfrom elementary linear regression models to very complex neural networks.Neural networks are currently limited by the size of data provided and the hugecomputational cost of training a model. This is especially problematic whendealing with a large set of features without much prior knowledge of how goodor bad each individual feature is. We try tackling the problem usingdimensionality reduction algorithms to construct more meaningful features. Wealso compare the accuracy and training times of raw data and data transformedafter dimensionality reduction to deduce a sufficient number of dimensionswithout sacrificing accuracy. The indicated estimation is done using a lighterdecision tree-based algorithm, AdaBoost, as it trains faster than neuralnetworks. We have chosen the data from an online database of topologicalmaterials, Materiae. Our final goal is to construct a model to predict thetopological properties of new materials from elementary properties.

Accelerated Neural Network Training through Dimensionality Reduction for High-Throughput Screening of Topological Materials