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Related papers: Mathematical Crystal Chemistry

200 papers

A routine crystallography technique, crystal structure analysis, is rarely performed in computational condensed matter research. The lack of methods to identify and characterize crystal structures reliably in particle simulation data…

Materials Science · Physics 2021-06-29 Michael Engel

We use numerical simulations to study the crystallization of monodisperse systems of hard aspherical particles. We find that particle shape and crystallizability can be easily related to each other when particles are characterized in terms…

Soft Condensed Matter · Physics 2015-05-18 William L. Miller , Behnaz Bozorgui , Angelo Cacciuto

Efficiently predicting properties of porous crystalline materials has great potential to accelerate the high throughput screening process for developing new materials, as simulations carried out using first principles model are often…

Machine Learning · Computer Science 2023-11-30 Marko Petković , Pablo Romero-Marimon , Vlado Menkovski , Sofia Calero

Colloidal model systems allow studying crystallization kinetics under fairly ideal conditions with rather well characterized pair interactions and minimized external influences. In complementary approaches therefore experiment, analytic…

Soft Condensed Matter · Physics 2015-06-19 Thomas Palberg

This paper develops a new continuous approach to a similarity between periodic lattices of ideal crystals. Quantifying a similarity between crystal structures is needed to substantially speed up the Crystal Structure Prediction, because the…

Computational Geometry · Computer Science 2024-09-05 Marco Michele Mosca , Vitaliy Kurlin

At high pressure, the typical behavior of elements dictated by the periodic table - including oxidation numbers, stoichiometries in compounds, and reactivity, to name but a few - is altered dramatically. As pressure is applied, the…

Materials Science · Physics 2022-05-11 Katerina P. Hilleke , Tiange Bi , Eva Zurek

Experimentally obtained X-ray diffraction (XRD) patterns can be difficult to solve, precluding the full characterization of materials, pharmaceuticals, and geological compounds. Herein, we propose a method based upon a multi-objective…

Materials Science · Physics 2024-07-09 Stefano Racioppi , Alberto Otero De la Roza , Samad Hajinazar , Eva Zurek

We study property prediction for crystal materials. A crystal structure consists of a minimal unit cell that is repeated infinitely in 3D space. How to accurately represent such repetitive structures in machine learning models remains…

Chemical Physics · Physics 2023-11-08 Yuchao Lin , Keqiang Yan , Youzhi Luo , Yi Liu , Xiaoning Qian , Shuiwang Ji

Crystal structures can be viewed as assemblies of space-filling polyhedra, which play a critical role in determining material properties such as ionic conductivity and dielectric constant. However, most conventional crystal structure…

Materials Science · Physics 2026-03-20 Tomoyasu Yokoyama , Kazuhide Ichikawa , Hisashi Naito

The paper describes an extension of the Liga algorithm for structure solution from atomic pair distribution function (PDF), to handle periodic crystal structures with multiple elements in the unit cell. The procedure is performed in 2…

Crystalline structure prediction is an essential prerequisite for designing materials with targeted properties. Yet, it is still an open challenge in materials design and drug discovery. Despite recent advances in computational materials…

Machine Learning · Computer Science 2025-09-29 Emmanuel Jehanno , Romain Menegaux , Julien Mairal , Sergei Grudinin

Atomic structure analysis of crystalline materials is a paramount endeavor in both chemical and material sciences. This sophisticated technique necessitates not only a solid foundation in crystallography but also a profound comprehension of…

Machine Learning · Computer Science 2026-04-27 Kaipeng Zheng , Weiran Huang , Wanli Ouyang , Han-Sen Zhong , Yuqiang Li

Crystal structure prediction algorithms have become powerful tools for materials discovery in recent years, however, they are usually limited to relatively small systems. The main challenge is that the number of local minima grows…

Materials Science · Physics 2022-02-09 Hao Gao , Junjie Wang , Yu Han , Jian Sun

Accurate structural analysis is essential to gain physical knowledge and understanding of atomic-scale processes in materials from atomistic simulations. However, traditional analysis methods often reach their limits when applied to…

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

Using molecular dynamics simulations, with a realistic many-body embedded-atom potential, and a novel method to characterize local order, we study the structure of pure nickel during the rapid quench of the liquid and in the resulting…

Disordered Systems and Neural Networks · Physics 2016-08-15 Oscar Rodríguez de la Fuente , José M. Soler

Crystal structure prediction has been a subject of topical interest, but remains a substantial challenge, especially for complex structures as it deals with the global minimization of the extremely rugged high-dimensional potential energy…

Materials Science · Physics 2022-01-26 Xuecheng Shao , Jian Lv , Peng Liu , Sen Shao , Pengyue Gao , Hanyu Liu , Yanchao Wang , Yanming Ma

The exploration of solid-solid phase transition suffers from the uncertainty of how atoms in two crystal structures match. We devised a theoretical framework to describe and classify crystal-structure matches (CSM). Such description fully…

Materials Science · Physics 2024-02-22 Fang-Cheng Wang , Qi-Jun Ye , Yu-Cheng Zhu , Xin-Zheng Li

Through extensive molecular simulations we determine a phase diagram of attractive, flexible polymer chains in two and three dimensions. A surprisingly rich collection of distinct crystal morphologies appear, which can be finely tuned…

Atomically thin 2-dimensional heterostructures are a promising, novel class of materials with groundbreaking properties. The possiblity of choosing the many constituent components and their proportions allows optimizing these materials to…

Mesoscale and Nanoscale Physics · Physics 2019-10-23 Petri Hirvonen , Vili Heinonen , Haikuan Dong , Zheyong Fan , Ken R. Elder , Tapio Ala-Nissila