Abstrait

Time-Frequency-Like Representation and Forward Design in Molecular Design Using Signal Processing and Machine Learning

Sergey Han

 The accumulation of molecular data from Quantum Mechanics (QM) theories such as Density Functional Theory (DFTQM) allows Machine Learning (ML) to speed up the discovery of new molecules, drugs, and materials. Models that combine QM and ML (QMML) have proven to be very effective in delivering QM precision at ML speed. In this paper, we show that by incorporating well-known Signal Processing (SP) techniques (such as short time Fourier transform, continuous wavelet analysis, and Wigner-Ville distribution) into the QMML pipeline, we can obtain a Powerful Machinery (QMSPML) that can be used for molecule representation, visualization, and forward design.