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Document type:
Zeitschriftenaufsatz 
Author(s):
Rahaman, O.; Gagliardi, A. 
Title:
Deep Learning Total Energies and Orbital Energies of Large Organic Molecules Using Hybridization of Molecular Fingerprints 
Abstract:
The ability to predict material properties without the need of resource consuming experimental efforts can immensely accelerate material and drug discovery. Although ab initio methods can be reliable and accurate in making suchpredictions, they are computationally too expensive at a large scale. The recent advancements in artificial intelligence and machine learning as well as availability of large quantum mechanics derived datasets enable us to train models on these datasets as benchmark and to...    »
 
Keywords:
Machine learning, graph neural network, many body tensor representation, molecular descriptors 
Journal title:
ChemRxiv 2020-06 
Year:
2020 
Year / month:
2020-06 
Quarter:
2. Quartal 
Month:
Jun 
Pages contribution:
1-11 
Publisher:
ChemRxiv Preprint