Danish Khan

University of Toronto — Canada

Materials Science · Physical Sciences

7h-index221citations27works5.82yr avg
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Research Topics

Machine Learning in Materials Science(19), Computational Drug Discovery Methods(10), Advanced Chemical Physics Studies(7), Crystallography and molecular interactions(2), Spectroscopy and Quantum Chemical Studies(2)

Publications27 total

SCAN based non-linear double hybrid density functional
The Journal of Chemical Physics·2025· 3 cited
Generalized convolutional many-body distribution functional representations
Proceedings of the National Academy of Sciences·2025· 1 citedOpen Access
Quantum mechanical dataset of 836k neutral closed-shell molecules with up to 5 heavy atoms from C, N, O, F, Si, P, S, Cl, Br
Scientific Data·2025· 4 citedOpen Access
Adapting hybrid density functionals with machine learning
Science Advances·2025· 19 citedOpen Access
Alchemical harmonic approximation based potential for iso-electronic diatomics: Foundational baseline for Δ-machine learning
The Journal of Chemical Physics·2025· 4 cited
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023
Chemical Science·2025· 12 citedOpen Access
Roadmap on Advancements of the FHI-aims Software Package
JuSER Publikationsportal·2025Open Access
Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023
Chemical Science·2025· 10 citedOpen Access
Generalized convolutional many body distribution functional representations
arXiv (Cornell University)·2024Open Access
Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Model Analysis in the TEA Challenge 2023
2024· 7 cited
Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Molecular Dynamics in the TEA Challenge 2023
2024· 5 cited
Alchemical harmonic approximation based potential for iso-electronic diatomics: Foundational baseline for $Δ$-machine learning
arXiv (Cornell University)·2024· 1 citedOpen Access
Reducing training data needs with minimal multilevel machine learning (M3L)
Machine Learning Science and Technology·2024· 18 citedOpen Access
Adaptive atomic basis sets
arXiv (Cornell University)·2024Open Access
Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials
Machine Learning Science and Technology·2024· 6 citedOpen Access
Adaptive hybrid density functionals
arXiv (Cornell University)·2024· 2 citedOpen Access
Ag‐based bimetallic clusters as catalysts for p‐nitrophenol reduction by glycerol: A <scp>DFT</scp> investigation
International Journal of Quantum Chemistry·2023· 3 citedOpen Access
Reducing Training Data Needs with Minimal Multilevel Machine Learning (M3L)
arXiv (Cornell University)·2023· 1 citedOpen Access
Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials
arXiv (Cornell University)·2023Open Access
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Frequent Co-authors

O. Anatole von Lilienfeld(16), Stefan Heinen(10), Alastair J. A. Price(8), Bing Huang(5), Jyoti Kuntail(4), Indrajit Sinha(4), Igor Poltavsky(4), Mirela Puleva(4), Anton Charkin-Gorbulin(4), Grégory Fonseca(4), Ilyes Batatia(4), Nicholas J. Browning(4), Stefan Chmiela(4), Mengnan Cui(4), J. Thorben Frank(4), Silvan Käser(4), Adil Kabylda(4), Carolin Müller(4), Kai Riedmiller(4), Kai Töpfer(4)