Time Frequency Representations and Deep Convolutional Neural Networks: A Recipe for Molecular Properties Prediction
| Autor: | Alain B. Tchagang, Julio J. Valdés |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Signal processing
Speedup Computer science Pipeline (computing) molecular forward design Convolutional neural network Time–frequency analysis Visualization Coulomb matrix convolutional neural networks Benchmark (computing) time-frequency representations Representation (mathematics) Algorithm density functional theory |
| Zdroj: | CCECE |
| Popis: | In recent years, Quantum Mechanics (QM) has been combined with Machine Learning (ML) algorithms to speed up the design of molecules, drugs and materials. These paradigms known as QM↔ML have been successful in providing the precision of QM at the speed of ML. In this work, we show that by integrating well-known signal processing (SP) techniques in the QM↔ML pipeline, we obtain a powerful methodology (QM↔SP↔ML) that can be used for representation, visualization and molecular properties predictions. Tested on the benchmark QM9 dataset, the new QM↔SP↔ML framework is able to predict the properties of molecules with a mean absolute error below acceptable chemical accuracy, and yield better or similar results compared to other ML state-of-the-art techniques described in the literature. 2021 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Sept 12-17, 2021, Held Virtually |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|