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Transition metal dichalcogenides (TMDs) are known to have a wide variety of magnetic structures by hosting other transition metal atoms in the van der Waals gaps. To understand the chemical trend of the magnetic properties of the…

Materials Science · Physics 2023-05-24 Tatsuto Hatanaka , Takuya Nomoto , Ryotaro Arita

Materials property predictions have improved from advances in machine learning algorithms, delivering materials discoveries and novel insights through data-driven models of structure-property relationships. Nearly all available models rely…

Materials Science · Physics 2022-04-13 Yiqun Wang , Xiao-Jie Zhang , Fei Xia , Elsa A. Olivetti , Ram Seshadri , James M. Rondinelli

The discovery of new multicomponent inorganic compounds can provide direct solutions to many scientific and engineering challenges, yet the vast size of the uncharted material space dwarfs current synthesis throughput. While the…

Computational Physics · Physics 2022-05-18 Sungwoo Kang , Wonseok Jeong , Changho Hong , Seungwoo Hwang , Youngchae Yoon , Seungwu Han

Polymer matrix composites exhibit remarkable lightweight and high strength properties that make them attractive for aerospace applications. Constituents' materials such as advanced polymers and fibers or fillers with their hierarchical…

Materials Science · Physics 2021-12-03 Satyajit Mojumder , Lei Tao , Ying Li , Wing Kam Liu

Electronic-structure theory is a strong pillar of materials science. Many different computer codes that employ different approaches are used by the community to solve various scientific problems. Still, the precision of different packages…

Materials discovery is a computationally intensive process that requires exploring vast chemical spaces to identify promising candidates with desirable properties. In this work, we propose using quantum-enhanced machine learning algorithms…

The past decade has seen rapid growth in the number of experimentally realized two-dimensional (2D) materials with diverse chemical and physical properties. However, information on their crystal structure, synthesis routes, and measured or…

Machine learning has the potential to accelerate materials discovery by accurately predicting materials properties at a low computational cost. However, the model inputs remain a key stumbling block. Current methods typically use…

Computational Physics · Physics 2021-01-07 Rhys E. A. Goodall , Alpha A. Lee

Current research on three-dimensional metamaterial has largely focused on conventional strut, plate, and shell-based lattice designs. Although these designs offer several advantages, they possess inherent limitations that can restrict their…

Computational Engineering, Finance, and Science · Computer Science 2026-03-12 Sahar Choukir , Nirosh Manohara , Chandra Veer Singh

The modeling of realistic magnetic materials requires the inclusion of defects. Based on the pseudospectral Landau-Lifshitz description of magnetisation dynamics, we propose a statistical model that takes into account defects, specifically…

Mesoscale and Nanoscale Physics · Physics 2026-03-12 C. Eagan , M. Copus , E. Iacocca

Chemical structure elucidation is a serious bottleneck in analytical chemistry today. We address the problem of identifying an unknown chemical threat given its mass spectrum and its chemical formula, a task which might take well trained…

Organic charge transfer salts show a variety of complex phases ranging from antiferromagnetic long-range order, spin liquid, bad metal or even superconductivity. A powerful method to investigate magnetism is spin-polarized inelastic neutron…

Materials Science · Physics 2014-07-29 Francesc Salvat-Pujol , Harald O. Jeschke , Roser Valenti

Nuclear Magnetic Resonance (NMR) spectroscopy is particularly well-suited to determine the structure of molecules and materials in powdered form. Structure determination usually proceeds by finding the best match between experimentally…

Determining, understanding, and predicting the so-called structure-property relation is an important task in many scientific disciplines, such as chemistry, biology, meteorology, physics, engineering, and materials science. Structure refers…

Machine Learning · Computer Science 2023-11-15 Binh Duong Nguyen , Pavlo Potapenko , Aytekin Dermici , Kishan Govind , Sébastien Bompas , Stefan Sandfeld

Topological data analysis (TDA) is a versatile tool that can be used to extract scientific knowledge from complex pattern formation processes. However, the physics correspondence between the features obtained from TDA and pattern dynamics…

Pattern Formation and Solitons · Physics 2024-07-09 Yoh-ichi Mototake , Masaichiro Mizumaki , Kazue Kudo , Kenji Fukumizu

This paper introduces the HEX (High-pressure Elemental Xstals) database, a complete database of the ground-state crystal structures of the first 57 elements of the periodic table, from H to La, at 0, 100, 200 and 300 GPa. HEX aims to…

Materials Science · Physics 2024-03-15 Federico Giannessi , Simone Di Cataldo , Santanu Saha , Lilia Boeri

Two dimensional square lattice general model of the magnetic dot array is introduced. In this model the intradot self-energy is predicted via the neural network and interdot magnetostatic coupling is approximated by the collection of…

Disordered Systems and Neural Networks · Physics 2009-11-07 Denis Horvath , Martin Gmitra , Ivo Vavra

Actinide and lanthanide-based materials display exotic properties that originate from the presence of itinerant or localized f-electrons and include unconventional superconductivity and magnetism, hidden order; and heavy fermion behavior.…

Materials Science · Physics 2021-02-26 Ayana Ghosh , Filip Ronning , Serge Nakhmanson , Jian-Xin Zhu

Crystal structures define how matter is organized at the atomic level. In the realm of crystalline inorganic materials, new structure types are rarely found, and most experimentally-realized structural motifs were established decades ago.…

Accurately predicting when and how materials fail is critical to designing safe, reliable structures, mechanical systems, and engineered components that operate under stress. Yet, fracture behavior remains difficult to model across the…

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