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Theoretical calculations of materials have in recent years shown promise in facilitating the analysis of convoluted experimental data. This is particularly invaluable in complex systems or for materials subject to certain environmental…

A method is suggested for estimation of structural properties of amorphous fullerene and its derivatives produced by vacuum annealing. The method is based on the fitting of the neutron or x-ray powder diffraction patterns for scattering…

Materials Science · Physics 2014-12-24 V. S. Neverov , P. A. Borisova , A. B. Kukushkin , V. V. Voloshinov

Compositional disorder is common in crystal compounds. In these compounds, some atoms are randomly distributed at some crystallographic sites. For such compounds, randomness forms many non-identical independent structures. Thus, calculating…

Materials Science · Physics 2022-12-23 Mostafa Yaghoobi , Mojtaba Alaei

Global optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space. The specific physical properties linked to a three-dimensional atomic arrangement make…

Possible crystalline modifications of chemical compounds at low temperatures correspond to local minima of the energy landscape. Determining these minima via simulated annealing is one method for the prediction of crystal structures, where…

Materials Science · Physics 2008-10-31 K. Doll , J. C. Schoen , M. Jansen

Multiphase powder X-ray diffraction (PXRD) analysis remains a fundamental bottleneck in structure identification, as real-world synthesis often produces complex mixtures whose constituent phases (components) cannot be reliably disentangled.…

Artificial Intelligence · Computer Science 2026-05-11 Hanyu Gao , Bin Cao , Yunyue Su , Tong-Yi Zhang , Qiang Liu

Recent advances in high-throughput experimentation for combinatorial studies have accelerated the discovery and analysis of materials across a wide range of compositions and synthesis conditions. However, many of the more powerful…

Powder diffraction is a primary structural characterization tool in materials science, yet automated phase identification remains a major bottleneck for autonomous discovery. Existing workflows rely heavily on search--match heuristics and…

Materials Science · Physics 2026-05-13 Lalit Yadav , Yongqiang Cheng , Mathieu Doucet

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

Classifying a crystalline solid's phase using X-ray diffraction (XRD) is a challenging endeavor, first because this is a poorly constrained problem as there are nearly limitless candidate phases to compare against a given experimental…

Applied Physics · Physics 2025-05-15 Kangyu Ji , Fang Sheng , Tianran Liu , Basita Das , Tonio Buonassisi

Volumetric crystal structure indexing and orientation mapping are key data processing steps for virtually any quantitative study of spatial correlations between the local chemistry and the microstructure of a material. For electron and…

Computational Physics · Physics 2020-09-03 Markus Kühbach , Matthew Kasemer , Baptiste Gault , Andrew Breen

Crystal truncation rods calculated in the kinematical approximation are shown to quantitatively agree with the sum of the diffracted waves obtained in the two-beam dynamical calculations for different reflections along the rod. The choice…

Other Condensed Matter · Physics 2018-01-11 Vladimir M. Kaganer

Coherent diffraction imaging enables the imaging of individual defects, such as dislocations or stacking faults, in materials.These defects and their surrounding elastic strain fields have a critical influence on the macroscopic properties…

Quantitative phase analysis is one of the major applications of X-ray powder diffraction. The essential principle of quantitative phase analysis is that the diffraction intensity of a component phase in a mixture is proportional to its…

Materials Science · Physics 2022-02-22 Hui Lia , Meng Hebcd , Ze Zhange

Development of new functional ceramics is important for several applications, including electrochemical batteries and fuel cells. Computational prescreening and selection of such materials can help discover novel materials but is…

Materials Science · Physics 2025-02-11 Keisuke Kameda , Takaaki Ariga , Kazuma Ito , Manabu Ihara , Sergei Manzhos

We propose a penalized likelihood method to jointly estimate multiple precision matrices for use in quadratic discriminant analysis and model based clustering. A ridge penalty and a ridge fusion penalty are used to introduce shrinkage and…

Machine Learning · Statistics 2014-05-06 Bradley S. Price , Charles J. Geyer , Adam J. Rothman

Conformal prediction (CP) is a distribution-free method to construct reliable prediction intervals that has gained significant attention in recent years. Despite its success and various proposed extensions, a significant practical feature…

Statistics Theory · Mathematics 2026-02-02 Louis Allain , Sébastien Da Veiga , Brian Staber

The multi-wavelength anomalous diffraction (MAD) method is used to determine phase information in x-ray crystallography by employing dispersion corrections from heavy atoms on coherent x-ray scattering. X-ray free-electron lasers (FELs)…

Atomic Physics · Physics 2011-11-18 Sang-Kil Son , Henry N. Chapman , Robin Santra

Constructing a quantum description of crystals from scattering experiments is of paramount importance to explain their macroscopic properties and to evaluate the pertinence of theoretical ab-initio models. While reconstruction methods of…

Materials Science · Physics 2019-04-19 Benjamin De Bruyne , Jean-Michel Gillet

High-throughput density-functional calculations of solids are extremely time consuming. As an alternative, we here propose a machine learning approach for the fast prediction of solid-state properties. To achieve this, LSDA calculations are…

Materials Science · Physics 2014-05-23 K. T. Schütt , H. Glawe , F. Brockherde , A. Sanna , K. R. Müller , E. K. U. Gross
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