English
Related papers

Related papers: Assessing the persistence of chalcogen bonds in so…

200 papers

Despite the uniquely high thermal conductivity of graphene is well known, the exploitation of graphene into thermally conductive nanomaterials and devices is limited by the inefficiency of thermal contacts between the individual nanosheets.…

Predicting the spatial configuration of gas molecules in nanopores of shale formations is crucial for fluid flow forecasting and hydrocarbon reserves estimation. The key challenge in these tight formations is that the majority of the pore…

For multilayer structures, interfacial failure is one of the most important elements related to device reliability. For cohesive zone modelling, traction-separation relations represent the adhesive interactions across interfaces. However,…

Computational Engineering, Finance, and Science · Computer Science 2023-01-02 Congjie Wei , Jiaxin Zhang , Kenneth M. Liechti , Chenglin Wu

The combination of neural network potential (NNP) with molecular simulations plays an important role in an efficient and thorough understanding of a molecular system's potential energy surface (PES). However, grasping the interplay between…

Computational Physics · Physics 2021-10-28 Ji Woong Yu , Min Young Ha , Bumjoon Seo , Won Bo Lee

Neural operators are capable of capturing nonlinear mappings between infinite-dimensional functional spaces, offering a data-driven approach to modeling complex functional relationships in classical density functional theory (cDFT). In this…

Due to efficient scaling with electron number N, density functional theory (DFT) is widely used for studies of large molecules and solids. Restriction of an exact mean-field theory to local potential functions has recently been questioned.…

Other Condensed Matter · Physics 2015-06-24 Robert K. Nesbet

Superfluidity is a generic feature of various quantum systems at low temperatures and it is in particular important for the description of dynamics of low energy nuclear reactions. The time-dependent density functional theory (TDDFT) is, to…

Nuclear Theory · Physics 2020-07-15 M. C. Barton , S. Jin , P. Magierski , K. Sekizawa , G. Wlazłowski , A. Bulgac

We suggest that the DNA molecules could form the cholesteric phase owing to an interaction mediated by the network of the hydrogen bonds (H-network) in the solvent. The model admits of the dependence of the optical activity of the solution…

Soft Condensed Matter · Physics 2009-10-31 V. L. Golo , E. I. Kats , Yu. M. Yevdokimov

We derive an exact representation of the exchange-correlation energy within density functional theory (DFT) which spawns a class of approximations leading to correct long-range asymptotic behavior. In what amounts to be the simplest…

Materials Science · Physics 2009-11-10 Roi Baer , Daniel Neuhauser

There is enormous recent interest in weak, van der Waals-type (vdW) interactions due to their fundamental relevance for two-dimensional materials and the so-called vdW heterostructures. Tackling this problem using computer simulation is…

Materials Science · Physics 2019-08-20 Fabian Schulz , Peter Liljeroth , Ari Paavo Seitsonen

We show that classical molecular density functional theory (MDFT), here in the homogeneous reference fluid approximation in which the functional is inferred from the properties of the bulk solvent, is a powerful new tool to study, at a…

Topological constraints (TCs) between polymers determine the behaviour of complex fluids such as creams, oils and plastics. Most of the polymer solutions used every day life employ linear chains; their behaviour is accurately captured by…

Soft Condensed Matter · Physics 2020-10-07 Fabio Landuzzi , Takenobu Nakamura , Davide Michieletto , Takahiro Sakaue

A neuroevolution potential (NEP) for the ternary $\alpha$-Fe--C--H system was developed based on a database generated from spin-polarized density functional theory (DFT) calculations, achieving empirical potential efficiency with DFT…

Materials Science · Physics 2025-10-23 Fan-Shun Meng , Shuhei Shinzato , Zhiqiang Zhao , Jun-Ping Du , Lei Gao , Zheyong Fan , Shigenobu Ogata

Understanding strongly correlated systems is essential for advancing quantum chemistry and materials science, yet conventional methods like Density Functional Theory (DFT) often fail to capture their complex electronic behavior. To address…

Chemical Physics · Physics 2025-09-01 Archith Rayabharam , N. R. Aluru

We present large-scale molecular dynamics (MD) simulations based on a machine-learning interatomic potential to investigate the wet etching behavior of various GaN facets in alkaline solution-a process critical to the fabrication of…

Materials Science · Physics 2025-05-14 Purun-hanul Kim , Jeong Min Choi , Seungwu Han , Youngho Kang

Binary metal clusters are of high interest for applications in heterogeneous catalysis and have received much attention in recent years. To gain insights into their structure and composition at the atomic scale, computer simulations can…

Chemical Physics · Physics 2020-04-10 Jan Weinreich , Anton Römer , Martín Leandro Paleico , Jörg Behler

Density functional theory (DFT) is one of the main methods in Quantum Chemistry that offers an attractive trade off between the cost and accuracy of quantum chemical computations. The electron density plays a key role in DFT. In this work,…

Chemical Physics · Physics 2018-09-11 Anton V. Sinitskiy , Vijay S. Pande

We theoretically investigate the quantum correlations (in terms of concurrence of indistinguishable electrons) in a prototype molecular system (hydrogen molecule). With the assistance of the standard approximations of the linear combination…

Quantum Physics · Physics 2016-11-18 Nikos Iliopoulos , Andreas F. Terzis

The adhesion between a polymer melt and substrate is studied in the presence of chemically attached chains on the substrate surface. Extensive molecular dynamics simulations have been carried out to study the effect of temperature, tethered…

Materials Science · Physics 2016-08-31 Scott W. Sides , Gary S. Grest , Mark J. Stevens

Hydrofluoroolefins are considered the most promising next-generation refrigerants due to their extremely low global warming potential values, which can effectively mitigate the global warming effect. However, the lack of reliable…

Machine Learning · Computer Science 2025-07-30 Gang Wang , Peng Hu