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.

Related papers

• Multi-task learning for molecular electronic structure approaching coupled-cluster accuracy [9.81014501502049]
  We develop a unified machine learning method for electronic structures of organic molecules using the gold-standard CCSD(T) calculations as training data. Tested on hydrocarbon molecules, our model outperforms DFT with the widely-used hybrid and double hybrid functionals in computational costs and prediction accuracy of various quantum chemical properties.
  arXiv  Detail & Related papers  (2024-05-09T19:51:27Z)
• Implementation of The Future of Drug Discovery: QuantumBased Machine Learning Simulation (QMLS) [8.469926863867904]
  We introduce our new concept QMLS to shorten the whole R&D phase three to six months and decrease the cost to merely fifty to eighty thousand USD. For Hit Generation, Machine Learning Molecule Generation (MLMG) generates possible hits according to the molecular structure of the target protein. For Lead Optimization, the resultant molecules generated and filtered from QSG and are compared, and molecules that appear as a result of both processes will be made into dozens of variations.
  arXiv  Detail & Related papers  (2023-08-14T13:18:40Z)
• QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules [69.25826391912368]
  We generate a new Quantum Hamiltonian dataset, named as QH9, to provide precise Hamiltonian matrices for 999 or 2998 molecular dynamics trajectories. We show that current machine learning models have the capacity to predict Hamiltonian matrices for arbitrary molecules.
  arXiv  Detail & Related papers  (2023-06-15T23:39:07Z)
• Molecular Geometry-aware Transformer for accurate 3D Atomic System modeling [51.83761266429285]
  We propose a novel Transformer architecture that takes nodes (atoms) and edges (bonds and nonbonding atom pairs) as inputs and models the interactions among them. Moleformer achieves state-of-the-art on the initial state to relaxed energy prediction of OC20 and is very competitive in QM9 on predicting quantum chemical properties.
  arXiv  Detail & Related papers  (2023-02-02T03:49:57Z)
• Exploring accurate potential energy surfaces via integrating variational quantum eigensovler with machine learning [8.19234058079321]
  We show in this work that variational quantum algorithms can be integrated with machine learning (ML) techniques. We encode the molecular geometry information into a deep neural network (DNN) for representing parameters of the variational quantum eigensolver (VQE)
  arXiv  Detail & Related papers  (2022-06-08T01:43:56Z)
• Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations [51.68332623405432]
  Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Recently, machine learned force fields (MLFFs) emerged as an alternative means to execute MD simulations. This work proposes a general approach to constructing accurate MLFFs for large-scale molecular simulations.
  arXiv  Detail & Related papers  (2022-05-17T13:08:28Z)
• Prediction of transport property via machine learning molecular movements [1.0554048699217666]
  We present a simple supervised machine learning method to predict the transport properties of materials. This method was applied to predict the viscosity of lubricant molecules in confinement with shear flow. We revealed two types of molecular mechanisms that contribute to low viscosity.
  arXiv  Detail & Related papers  (2022-03-07T02:28:07Z)
• Chemical-Reaction-Aware Molecule Representation Learning [88.79052749877334]
  We propose using chemical reactions to assist learning molecule representation. Our approach is proven effective to 1) keep the embedding space well-organized and 2) improve the generalization ability of molecule embeddings. Experimental results demonstrate that our method achieves state-of-the-art performance in a variety of downstream tasks.
  arXiv  Detail & Related papers  (2021-09-21T00:08:43Z)
• Computing molecular excited states on a D-Wave quantum annealer [52.5289706853773]
  We demonstrate the use of a D-Wave quantum annealer for the calculation of excited electronic states of molecular systems. These simulations play an important role in a number of areas, such as photovoltaics, semiconductor technology and nanoscience.
  arXiv  Detail & Related papers  (2021-07-01T01:02:17Z)
• BIGDML: Towards Exact Machine Learning Force Fields for Materials [55.944221055171276]
  Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Here, we introduce the Bravais-Inspired Gradient-Domain Machine Learning approach and demonstrate its ability to construct reliable force fields using a training set with just 10-200 atoms.
  arXiv  Detail & Related papers  (2021-06-08T10:14:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.