Molecular Design Using Signal Processing and Machine Learning: Time-Frequency-like Representation and Forward Design

• URL: http://arxiv.org/abs/2004.10091v3
• Date: Tue, 3 Nov 2020 15:18:38 GMT
• Title: Molecular Design Using Signal Processing and Machine Learning: Time-Frequency-like Representation and Forward Design
• Authors: Alain B. Tchagang, Ahmed H. Tewfik, and Julio J. Vald\'es
• Abstract summary: We show that by integrating well-known signal processing techniques in the QM-ML pipeline, we obtain a powerful machinery (QM-SP-ML) In this study, we show that the time-frequency-like representation of molecules encodes their structural, geometric, energetic, electronic and thermodynamic properties. Tested on the QM9 dataset (composed of 133,855 molecules and 19 properties), the new QM-SP-ML model is able to predict the properties of molecules with a mean absolute error (MAE) below acceptable chemical accuracy.
• Score: 9.986608420951558
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accumulation of molecular data obtained from quantum mechanics (QM) theories such as density functional theory (DFTQM) make it possible for machine learning (ML) to accelerate the discovery of new molecules, drugs, and materials. Models that combine QM with ML (QM-ML) have been very effective in delivering the precision of QM at the high speed of ML. In this study, we show that by integrating well-known signal processing (SP) techniques (i.e. short time Fourier transform, continuous wavelet analysis and Wigner-Ville distribution) in the QM-ML pipeline, we obtain a powerful machinery (QM-SP-ML) that can be used for representation, visualization and forward design of molecules. More precisely, in this study, we show that the time-frequency-like representation of molecules encodes their structural, geometric, energetic, electronic and thermodynamic properties. This is demonstrated by using the new representation in the forward design loop as input to a deep convolutional neural networks trained on DFTQM calculations, which outputs the properties of the molecules. Tested on the QM9 dataset (composed of 133,855 molecules and 19 properties), the new QM-SP-ML model is able to predict the properties of molecules with a mean absolute error (MAE) below acceptable chemical accuracy (i.e. MAE < 1 Kcal/mol for total energies and MAE < 0.1 ev for orbital energies). Furthermore, the new approach performs similarly or better compared to other ML state-of-the-art techniques described in the literature. In all, in this study, we show that the new QM-SP-ML model represents a powerful technique for molecular forward design. All the codes and data generated and used in this study are available as supporting materials at https://github.com/TABeau/QM-SP-ML.

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