nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset-Reference-Cited by-同舟云学术

nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset

Published:2022 Issue:42 Volume:24 Page:25853-25863
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Khrabrov Kuzma¹^ORCID,Shenbin Ilya²^ORCID,Ryabov Alexander³⁴^ORCID,Tsypin Artem¹^ORCID,Telepov Alexander¹^ORCID,Alekseev Anton²⁵^ORCID,Grishin Alexander¹^ORCID,Strashnov Pavel¹^ORCID,Zhilyaev Petr³,Nikolenko Sergey²⁶^ORCID,Kadurin Artur¹⁷^ORCID

Affiliation:

1. AIRI, Kutuzovskiy prospect house 32 building K.1, Moscow, 121170, Russia

2. St. Petersburg Department of Steklov Mathematical Institute of Russian Academy of Sciences, nab. r. Fontanki 27, St. Petersburg 191011, Russia

3. Center for Materials Technologies, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, bld. 1, Moscow, 121205, Russia

4. Moscow Institute of Physics and Technology (National Research University), Institutsky lane, 9, Dolgoprudny, Moscow Region 141700, Russia

5. St. Petersburg University, 7-9 Universitetskaya Embankment, St Petersburg, 199034, Russia

6. ISP RAS Research Center for Trusted Artificial Intelligence, Alexander Solzhenitsyn st. 25, Moscow, 109004, Russia

7. Kuban State University, Stavropolskaya Street, 149, Krasnodar 350040, Russia

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.

Funder

Analytical Center for the Government of the Russian Federation

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2022/CP/D2CP03966D

Reference74 articles.

1. B. L.Hammond , W. A.Lester and P. J.Reynolds , Monte Carlo methods in ab initio quantum chemistry , World Scientific , 1994 , vol. 1

2. Inhomogeneous Electron Gas

3. Self-Consistent Equations Including Exchange and Correlation Effects

4. R. M.Martin , Electronic structure: basic theory and practical methods , Cambridge university press , 2020

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Quantum-Informed Molecular Representation Learning Enhancing ADMET Property Prediction;Journal of Chemical Information and Modeling;2024-06-25

2. Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting;Nature Communications;2024-02-26

3. Graph neural networks for predicting structural stability of Cd- and Zn-doped γ-CsPbI3;Computational Materials Science;2024-01

4. Data‐Driven Compound Identification in Atmospheric Mass Spectrometry;Advanced Science;2023-12-14

5. Modelling local and general quantum mechanical properties with attention-based pooling;Communications Chemistry;2023-11-29