English
Related papers

Related papers: An experimentally validated end-to-end framework f…

200 papers

Universal machine-learned interatomic potentials (U-MLIPs) have demonstrated broad applicability across diverse atomistic systems but often require fine-tuning to achieve task-specific accuracy. While the number of available U-MLIPs and…

Computational Physics · Physics 2025-08-25 Xiaoqing Liu , Kehan Zeng , Zedong Luo , Yangshuai Wang , Teng Zhao , Zhenli Xu

The performance of all-solid-state battery (ASSB) cathodes strongly depends on their microstructure. Optimizing the cathode morphology can therefore enhance effective macroscopic properties such as ionic and electronic conductivity. The…

Computational modeling is an important aspect of the research on nuclear waste materials. In particular, atomistic simulations, when used complementary to experimental efforts, contribute to the scientific basis of safety case for nuclear…

Metal--organic frameworks (MOFs) are a family of chemically diverse materials, with applications in a wide range of fields covering engineering, physics, chemistry, biology and medicine. Research so far has focused almost entirely on…

Electronic circular dichroism is an important optical phenomenon offering insights into chiral molecular materials. On the other hand, metal-organic frameworks (MOFs) are a novel group of crystalline porous thin-film materials that provide…

Universal machine-learned interatomic potentials (uMLIPs) offer a promising approach to performing atomistic simulations at near-DFT accuracy with greatly reduced computational cost. Here, we present a new high-temperature benchmarking…

Materials Science · Physics 2026-04-29 Connor W. Edwards , Jack D. Evans

An approach for incorporating embedded simulation and analysis capabilities in complex simulation codes through template-based generic programming is presented. This approach relies on templating and operator overloading within the C++…

Mathematical Software · Computer Science 2012-05-17 Roger P. Pawlowski , Eric T. Phipps , Andrew G. Salinger

The number of published Machine Learning Interatomic Potentials (MLIPs) has increased significantly in recent years. These new data-driven potential energy approximations often lack the physics-based foundations that inform many…

Materials Science · Physics 2024-04-02 Kimia Ghaffari , Salil Bavdekar , Douglas E. Spearot , Ghatu Subhash

In studying solidification process by simulations on the atomic scale, the modeling of crystal nucleation or amorphisation requires the construction of interatomic interactions that are able to reproduce the properties of both the solid and…

The design of structural & functional materials for specialized applications is being fueled by rapid advancements in materials synthesis, characterization, manufacturing, with sophisticated computational materials modeling frameworks that…

Spinodal metamaterials, with architectures inspired by natural phase-separation processes, have presented a significant alternative to periodic and symmetric morphologies when designing mechanical metamaterials with extreme performance.…

Computational Engineering, Finance, and Science · Computer Science 2025-01-10 Prakash Thakolkaran , Michael A. Espinal , Somayajulu Dhulipala , Siddhant Kumar , Carlos M. Portela

In this introductory review, we give an overview of the computational chemistry methods commonly used in the field of metal-organic frameworks (MOFs), to describe or predict the structures themselves and characterize their various…

Materials Science · Physics 2016-02-02 François-Xavier Coudert , Alain H. Fuchs

We discuss an inverse approach for atomistic modeling of glassy materials. The focus is on structural modeling and electronic properties of hydrogenated amorphous silicon and glassy GeSe2 alloy. The work is based upon a new approach…

Disordered Systems and Neural Networks · Physics 2009-11-13 Parthapratim Biswas , D. A. Drabold

Understanding the mechanical properties of solid-state materials at the atomic scale is crucial for developing novel materials. For example, amorphous LiSi alloys are attractive anode materials for solid-state Li-ion batteries but face…

Disordered Systems and Neural Networks · Physics 2024-02-15 Zixiong Wei , Nongnuch Artrith

Though offering unprecedented pathways to molecular dynamics (MD) simulations of technologically-relevant materials and conditions, machine-learning interatomic potentials (MLIPs) are typically trained for ``simple'' materials and…

Materials Science · Physics 2025-07-09 Nikola Koutná , Shuyao Lin , Lars Hultman , Davide G. Sangiovanni , Paul H. Mayrhofer

Accurate predictions of thermo-mechanically coupled process in metals can lead to a reduction of cost and an increase of productivity in manufacturing processes such as forming. For modeling these coupled processes with the finite element…

Materials Science · Physics 2019-05-30 Jifeng Li , Ignacio Romero , Javier Segurado

Si and its oxides have been extensively explored in theoretical research due to their technological and industrial importance. Simultaneously describing interatomic interactions within both Si and SiO$_2$ without the use of \textit{ab…

Materials Science · Physics 2024-07-29 Karim Zongo , Hao Sun , Claudiane Ouellet-Plamondon , Laurent Karim Béland

Layered "mosaic" metal-halide perovskite materials display a wide-variety of microstructures that span the order-disorder spectrum and can be tuned via the composition of their constituent B-site octahedral species. Such materials are…

Materials Science · Physics 2025-11-25 Murray Skolnick , Salvatore Torquato

Twisted van der Waals heterostructures have latterly received prominent attention for their many remarkable experimental properties, and the promise that they hold for realising elusive states of matter in the laboratory. We propose that…

‹ Prev 1 3 4 5 6 7 10 Next ›