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Title:

Deep Learning Total Energies and Orbital Energies of Large Organic Molecules Using Hybridization of Molecular Fingerprints

Document type:
Zeitschriftenaufsatz
Author(s):
Rahaman, O.; Gagliardi, A.
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
Fulltext / DOI:
doi:10.26434/chemrxiv.12581381.v1
WWW:
https://chemrxiv.org/articles/preprint/Deep_Learning_Total_Energies_and_Orbital_Energies_of_Large_Organic_Molecules_Using_Hybridization_of_Molecular_Fingerprints/12581381
Publisher:
ChemRxiv Preprint
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