Published in
Royal Society of Chemistry, Physical Chemistry Chemical Physics, 42(24), p. 25853-25863, 2022
DOI: 10.1039/d2cp03966d
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nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset
Journal article published in 2022 by
Kuzma Khrabrov 
,
Ilya Shenbin ,
Alexander Ryabov ,
Artem Tsypin ,
Alexander Telepov ,
Anton Alekseev ,
Alexander Grishin ,
Pavel Strashnov ,
Petr Zhilyaev,
Sergey Nikolenko ,
Artur Kadurin
,
Ilya Shenbin ,
Alexander Ryabov ,
Artem Tsypin ,
Alexander Telepov ,
Anton Alekseev ,
Alexander Grishin ,
Pavel Strashnov ,
Petr Zhilyaev,
Sergey Nikolenko ,
Artur Kadurin
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Abstract
In this work we present nablaDFT, the new dataset and benchmark for the Density Functional Theory Hamiltonian and energy prediction. We provide data for over 1 million different molecules and over 5 million conformations and baseline models for both tasks.