scholarly journals Correction to Benchmarking Coordination Number Prediction Algorithms on Inorganic Crystal Structures

2021 ◽  
Author(s):  
Hillary Pan ◽  
Alex M. Ganose ◽  
Matthew Horton ◽  
Muratahan Aykol ◽  
Kristin Persson ◽  
...  
Keyword(s):  
Crystal Structures ◽  
Coordination Number ◽  
Prediction Algorithms ◽  
Inorganic Crystal

scholarly journals Benchmarking Coordination Number Prediction Algorithms on Inorganic Crystal Structures

2021 ◽  
Vol 60 (3) ◽  
pp. 1590-1603
Author(s):  
Hillary Pan ◽  
Alex M. Ganose ◽  
Matthew Horton ◽  
Muratahan Aykol ◽  
Kristin A. Persson ◽  
...  
Keyword(s):  
Crystal Structures ◽  
Coordination Number ◽  
Prediction Algorithms ◽  
Inorganic Crystal

scholarly journals Benchmarking Coordination Number Prediction Algorithms on Inorganic Crystal Structures

2021 ◽  
Author(s):  
Hillary Pan ◽  
Alex Ganose ◽  
Matthew Horton ◽  
Muratahan Aykol ◽  
Kristin Persson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Crystal Structures ◽  
Near Neighbor ◽  
Research Literature ◽  
Small Perturbations ◽  
Materials Properties ◽  
Coordination Numbers ◽  
Prediction Algorithms ◽  
Inorganic Crystal ◽  
Benchmark Suite

Coordination numbers and geometries form a theoretical framework for understanding and predicting materials properties. Algorithms to determine coordination numbers automatically are increasingly used for machine learning and automatic structural analysis. In this work, we introduce MaterialsCoord, a benchmark suite containing 56 experimentally-derived crystal structures (spanning elements, binaries, and ternary compounds) and their corresponding coordination environments as described in the research literature. We also describe CrystalNN, a novel algorithm for determining near neighbors. We compare CrystalNN against 7 existing near-neighbor algorithms on the MaterialsCoord benchmark, finding CrystalNN to perform similarly to several well-established algorithms. For each algorithm, we also assess computational demand and sensitivity towards small perturbations that mimic thermal motion. Finally, we investigate the similarity between bonding algorithms when applied to the Materials Project database. We expect that this work will aid the development of coordination prediction algorithms as well as improve structural descriptors for machine learning and other applications.

scholarly journals Benchmarking Coordination Number Prediction Algorithms on Inorganic Crystal Structures

2021 ◽  
Author(s):  
Hillary Pan ◽  
Alex Ganose ◽  
Matthew Horton ◽  
Muratahan Aykol ◽  
Kristin Persson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Crystal Structures ◽  
Near Neighbor ◽  
Research Literature ◽  
Small Perturbations ◽  
Materials Properties ◽  
Coordination Numbers ◽  
Prediction Algorithms ◽  
Inorganic Crystal ◽  
Benchmark Suite

Coordination numbers and geometries form a theoretical framework for understanding and predicting materials properties. Algorithms to determine coordination numbers automatically are increasingly used for machine learning and automatic structural analysis. In this work, we introduce MaterialsCoord, a benchmark suite containing 56 experimentally-derived crystal structures (spanning elements, binaries, and ternary compounds) and their corresponding coordination environments as described in the research literature. We also describe CrystalNN, a novel algorithm for determining near neighbors. We compare CrystalNN against 7 existing near-neighbor algorithms on the MaterialsCoord benchmark, finding CrystalNN to perform similarly to several well-established algorithms. For each algorithm, we also assess computational demand and sensitivity towards small perturbations that mimic thermal motion. Finally, we investigate the similarity between bonding algorithms when applied to the Materials Project database. We expect that this work will aid the development of coordination prediction algorithms as well as improve structural descriptors for machine learning and other applications.

scholarly journals Benchmarking Coordination Number Prediction Algorithms on Inorganic Crystal Structures

2020 ◽  
Author(s):  
Hillary Pan ◽  
Alex Ganose ◽  
Matthew Horton ◽  
Muratahan Aykol ◽  
Kristin Persson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Crystal Structures ◽  
Near Neighbor ◽  
Research Literature ◽  
Small Perturbations ◽  
Materials Properties ◽  
Coordination Numbers ◽  
Prediction Algorithms ◽  
Inorganic Crystal ◽  
Benchmark Suite

Coordination numbers and geometries form a theoretical framework for understanding and predicting materials properties. Algorithms to determine coordination numbers automatically are increasingly used for machine learning and automatic structural analysis. In this work, we introduce MaterialsCoord, a benchmark suite containing 56 experimentally-derived crystal structures (spanning elements, binaries, and ternary compounds) and their corresponding coordination environments as described in the research literature. We also describe CrystalNN, a novel algorithm for determining near neighbors. We compare CrystalNN against 7 existing near-neighbor algorithms on the MaterialsCoord benchmark, finding CrystalNN to be the most accurate overall. For each algorithm, we also assess computational demand and sensitivity towards small perturbations that mimic thermal motion. Finally, we investigate the similarity between bonding algorithms when applied to the Materials Project database. We expect that this work will aid the development of coordination prediction algorithms and improve the accuracy of structural descriptors for machine learning and other applications.

scholarly journals The Principle of Maximal Simplicity for Modular Inorganic Crystal Structures

Crystals ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1472
Author(s):  
Sergey V. Krivovichev
Keyword(s):  
Crystal Structures ◽  
Structure Prediction ◽  
Structural Information ◽  
Structural Complexity ◽  
Configurational Entropy ◽  
Sensu Stricto ◽  
Least Action ◽  
Inorganic Crystal ◽  
Principle Of Least Action ◽  
Structure Description

Modularity is an important construction principle of many inorganic crystal structures that has been used for the analysis of structural relations, classification, structure description and structure prediction. The principle of maximal simplicity for modular inorganic crystal structures can be formulated as follows: in a modular series of inorganic crystal structures, the most common and abundant in nature and experiments are those arrangements that possess maximal simplicity and minimal structural information. The latter can be quantitatively estimated using information-based structural complexity parameters. The principle is applied for the modular series based upon 0D (lovozerite family), 1D (biopyriboles) and 2D (spinelloids and kurchatovite family) modules. This principle is empirical and is valid for those cases only, where there are no factors that may lead to the destabilization of simplest structural arrangements. The physical basis of the principle is in the relations between structural complexity and configurational entropy sensu stricto (which should be distinguished from the entropy of mixing). It can also be seen as an analogy of the principle of least action in physics.

Basic Inorganic Crystal Structures and Materials

2008 ◽  
pp. 364-396
Author(s):  
Wai-Kee Li ◽  
Gong-Du Zhou ◽  
Thomas Chung Wai Mak
Keyword(s):  
Crystal Structures ◽  
Inorganic Crystal

Organolanthanoids. XI. The Crystal and Molecular Structures of Two Tris(η5-cyclopentadienyl)(pyridine) lanthanoid(III) Compounds

1987 ◽  
Vol 40 (5) ◽  
pp. 907 ◽  
Author(s):  
GB Deacon ◽  
BM Gatehouse ◽  
SN Platts ◽  
DL Wilkinson
Keyword(s):  
Crystal Structures ◽  
Coordination Number ◽  
Unit Cell ◽  
Molecular Structures ◽  
Monoclinic Space Group ◽  
Space Group ◽  
Crystal And Molecular Structures

The crystal structures of tris (η5-cyclopentadienyl) (pyridine) samarium(III), monoclinic, space group P21/c, a 10.906(4), b 8.636(2), c 17.825(3) �, β 96.44(2)�, Z 4, R 0.027 and Rw 0.032 for 3619 'observed' reflections, and tris (η5-cyclopentadienyl)(pyridine)neodymium(III), monoclinic, space group P21 / c, a 14-206(4), b 8.619(2), c 15.190(7) �, β 107.38(2)�, Z 4, R 0.035 and R, 0.039 for 2677 'observed' reflections have been determined. Both compounds have pseudotetrahedral geometry with a coordination number of 10 for the lanthanoid metal but there is a difference in the coordination of pyridine and in unit cell packing between the two structures.

Cellular automata modeling of complex inorganic crystal structures

2010 ◽  
Vol 66 (a1) ◽  
pp. s40-s40
Author(s):  
Sergey V. Krivovichev ◽  
Vladislav V. Gurzhiy ◽  
Ivan G. Tananaev ◽  
Boris F. Myasoedov
Keyword(s):  
Cellular Automata ◽  
Crystal Structures ◽  
Inorganic Crystal

A comparison of the crystal structures of Sb2Tl7, Cu5Zn8 (γ-brass), and Ir3Ge7

1980 ◽  
Vol 58 (7) ◽  
pp. 708-713 ◽  
Author(s):  
Erwin Hellner ◽  
Elke Koch
Keyword(s):  
Crystal Structures ◽  
Coordination Number ◽  
Tetrahedral Voids ◽  
Atomic Parameters

The crystal structures of Sb2Tl7, Cu5Zn8, and Ir3Ge7 are discussed with the aid of shortest distances of Dirichlet domains and of voids in their frameworks. It will be pointed out that all three frameworks must be regarded as different though relations between the atomic parameters are obvious. The Sb2Tl7 structure represents a superstructure of an I lattice. The frameworks of Sb2Tl7 and of Cu5Zn8 contain tetrahedral voids only, while in Ir3Ge7 voids with a larger coordination number also exist.

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