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We present an orbital-resolved extension of the Hubbard $U$ correction to density-functional theory (DFT). Compared to the conventional shell-averaged approach, the prediction of energetic, electronic and structural properties is strongly…

Materials Science · Physics 2024-09-18 Eric Macke , Iurii Timrov , Nicola Marzari , Lucio Colombi Ciacchi

Metal-organic frameworks (MOFs) can be exceptionally good catalytic materials thanks to the presence of active metal centres and a porous structure that is advantageous for molecular adsorption and confinement. We present here a…

Metal-organic frameworks are a novel family of chemically diverse materials, which are of interest across engineering, physics, chemistry, biology, and medicine-based disciplines. Since the development of the field in its current form more…

Materials Science · Physics 2019-04-23 Thomas D. Bennett , Anthony K. Cheetham , Alain H. Fuchs , François-Xavier Coudert

Metal-Organic frameworks (MOFs) are promising candidates for advanced photocatalytically active materials. These porous crystalline compounds have large active surface areas and structural tunability and are thus highly competitive with…

In this study, we explore the potential of using quantum natural language processing (QNLP) to inverse design metal-organic frameworks (MOFs) with targeted properties. Specifically, by analyzing 450 hypothetical MOF structures consisting of…

Machine Learning · Computer Science 2025-11-21 Shinyoung Kang , Jihan Kim

Data-driven modal decompositions are useful tools for compressing data or identifying dominant structures. Popular ones like the dynamic mode decomposition (DMD) and the proper orthogonal decomposition (POD) are defined with continuous…

Fluid Dynamics · Physics 2025-11-06 Manuel Ratz , Alessandro Parente , Miguel Alfonso Mendez

The metallicity structure of stellar halos encodes the fossil record of galaxy assembly, tracing the chemical evolution and dynamical imprint of past mergers. Using five Milky Way-mass halos from the Aquarius simulations, we introduce an…

Astrophysics of Galaxies · Physics 2025-12-01 Amit Mondal , Biswajit Pandey

One important yet exceedingly rare property of the cuprate high-temperature superconductors is the presence of a single correlated $d$ band in the low-energy spectrum, leading to the one-band Hubbard model as the minimal description. In…

Strongly Correlated Electrons · Physics 2019-06-05 Eric B. Isaacs , Chris Wolverton

Covalent-organic frameworks (COFs) are intriguing platforms for designing functional molecular materials. Here, we present a computational study based on van der Waals dispersion-corrected hybrid density functional theory calculations to…

Materials Science · Physics 2017-03-09 Srimanta Pakhira , Kevin P. Lucht , Jose L. Mendoza-Cortes

The mechanical properties of metal-organic frameworks (MOFs) are of high fundamental and also practical relevance. A particularly intriguing technique for determining anisotropic elastic tensors is Brillouin scattering, which so far has…

Physisorption of hydrogen molecules in metal-organic frameworks (MOFs) provides a promising way for hydrogen storage, in which the van der Waals (vdW) interaction plays an important role but cannot described by the density functional theory…

Materials Science · Physics 2012-10-17 Yue Huang , San-Huang Ke

While density functional theory (DFT) serves as a prevalent computational approach in electronic structure calculations, its computational demands and scalability limitations persist. Recently, leveraging neural networks to parameterize the…

Computational Physics · Physics 2024-06-18 Yang Zhong , Hongyu Yu , Jihui Yang , Xingyu Guo , Hongjun Xiang , Xingao Gong

We present a computational microscopy analysis (targeted molecular dynamics simulations) of the structure and performance of conductive metal organic framework (MOF) electrodes in supercapacitors with room temperature ionic liquids. The…

Materials Science · Physics 2020-02-24 Sheng Bi , Ming Chen , Runxi Wang , Jiamao Feng , Mircea Dinca , Alexei A. Kornyshev , Guang Feng

The fundamental relationship of the atomic structure (represented by its atomic property parameters, APPs) and its physical properties of a specific inorganic substance can be realized in the bottom-up data-centric and the top-down…

Materials Science · Physics 2021-05-28 Pierre Villars , Evgeny Blokhin , Shuichi Iwata

Crystal-graph attention networks have emerged recently as remarkable tools for the prediction of thermodynamic stability and materials properties from unrelaxed crystal structures. Previous networks trained on two million materials…

Advancements of both computational and experimental tools have recently led to significant progress in the development of new advanced and functional materials, paralleled by a quick growth of the overall amount of data and information on…

Materials Science · Physics 2024-12-25 Fabio Le Piane , Matteo Baldoni , Mauro Gaspari , Francesco Mercuri

The accurate prediction of the electronic properties of materials at a low computational expense is a necessary conditions for the development of effective high-throughput quantum-mechanics (HTQM) frameworks for accelerated materials…

Strongly Correlated Electrons · Physics 2014-10-22 Luis A. Agapito , Stefano Curtarolo , Marco Buongiorno Nardelli

In organic proton-transfer ferroelectrics (OPTFe), molecules are linked together in a hydrogen-bonded network and proton transfer (PT) between molecules is the dominant mechanism of ferroelectric switching. Their fast switching frequencies…

As semiconductor technologies continue to scale down to the nanoscale, the efficient prediction of material properties becomes increasingly critical. The tight-binding (TB) method is a widely used semi-empirical approach that offers a…

Materials Science · Physics 2025-11-27 In Jun Park , Kamal Choudhary

High-throughput computational screening (HTCS) of gas adsorption in metal-organic frameworks (MOFs) typically relies on classical generic force fields such as the Universal Force Field (UFF), which are efficient but often fail to capture…