22 citations · 59 across the 10 of their papers we have counts for
12 papers
SU(2)-Equivariant Spinor Message Passing for Molecular Magnetism
Erh-Wei Sheng, Beom Seok Kang, Yuchao Lin +2
Predicting molecular magnetic properties often requires costly electronic-structure calculations that account for spin-orbit coupling. Inspired by the spinor nature of the underlyi…
Beyond DFT electronic band structures via dual-space orbital learning
Minhyuk Kang, Beom Seok Kang, Nikolai Argatoff +4
Accurate electronic band structures are essential for predicting material properties, but high-fidelity calculations are computationally demanding. We introduce O-kRystal, a dual-s…
Learning the Kohn-Sham map with neural operators for quasi-linear scaling density functional theory
Danish Khan, Maurice D. Hanisch, Nikolai Argatoff +3
Kohn--Sham density functional theory (DFT) underpins electronic-structure simulations, but repeated orbital diagonalizations lead to cubic scaling, restricting quantum calculations…
Roadmap on Advancements of the FHI-aims Software Package
Joseph W. Abbott, Carlos Mera Acosta, Alaa Akkoush +203
Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accurac…
Generalized convolutional many body distribution functional representations
Danish Khan, O. Anatole von Lilienfeld
Modern machine learning (ML) models of chemical and materials systems with billions of parameters require vast training datasets and considerable computational efforts. Lightweight…
Alchemical harmonic approximation based potential for iso-electronic diatomics: Foundational baseline for -machine learning
Simon León Krug, Danish Khan, O. Anatole von Lilienfeld
We introduce the alchemical harmonic approximation (AHA) of the absolute electronic energy for charge-neutral iso-electronic diatomics at fixed interatomic distance . To accou…
Quantum mechanical dataset of 836k neutral closed shell molecules with upto 5 heavy atoms from CNOFSiPSClBr
Danish Khan, Anouar Benali, Scott Y. H. Kim +2
We introduce the Vector-QM24 (VQM24) dataset comprehensively covering all possible neutral closed-shell small organic and inorganic molecules with up to five heavy (\textit{p}-bloc…
Adaptive atomic basis sets
Danish Khan, Maximilian L. Ach, O. Anatole von Lilienfeld
Atomic basis sets are widely employed within quantum mechanics based simulations of matter. We introduce a machine learning model that adapts the basis set to the local chemical en…
Adaptive hybrid density functionals
Danish Khan, Alastair James Arthur Price, Maximilian L. Ach +1
Exact exchange contributions are known to crucially affect electronic states, which in turn govern covalent bond formation and breaking in chemical species. Empirically averaging t…
Reducing Training Data Needs with Minimal Multilevel Machine Learning (M3L)
Stefan Heinen, Danish Khan, Guido Falk von Rudorff +7
For many machine learning applications in science, data acquisition, not training, is the bottleneck even when avoiding experiments and relying on computation and simulation. Corre…
Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials
Siwoo Lee, Stefan Heinen, Danish Khan +1
We present an automated data-collection pipeline involving a convolutional neural network and a large language model to extract user-specified tabular data from peer-reviewed liter…
Kernel based quantum machine learning at record rate : Many-body distribution functionals as compact representations
Danish Khan, Stefan Heinen, O. Anatole von Lilienfeld
The feature vector mapping used to represent chemical systems is a key factor governing the superior data-efficiency of kernel based quantum machine learning (QML) models applicabl…