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Improving molecular design through a machine learning approach
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DOI：10.1016/j.cep.2020.108173.png)
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摘要
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We present a study for predicting 15 molecular properties through the combination of a quantum mechanical database, taken from the quantum chemistry QM9 database, and feed forward deep neural networks approaches. The aim of the work is to show that the combination of a priori computed ab-initio information and machine learning can support experimental work to speed up the discovery and formulation of novel compounds. The importance of this work also relies on the fact that through this computer-aided molecular design approach no approximate or heuristic contribution methods are needed for physical and thermodynamic properties information. We show that using proper hyper-parameters tuning of deep neural networks is possible, even with modest computational resources, to design the chemical structure of compounds matching target molecular properties making them feasible for practical industrial applications in diverse areas such as energy, water, food, health and transport economical sectors.
Keyword:
Quantum chemistry: molecular properties
Machine learning
Deep neural networks
Optimization
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