Multipopulation Genetic Algorithm for Simulation of the Crystal Structure from X-Ray Diffraction Data

Предложен мультипопуляционный параллельный генетический алгоритм для моделирования атомной кристаллической структуры химических соединений из данных рентгеновской порошковой дифракции. Индивидуальные генетические алгоритмы выполняются на разных узлах вычислительного кластера. Лучшие структурные модели из всех узлов подвергаются локальной оптимизации по методу полнопрофильного структурного анализа и накапливаются на управляющем узле. Он управляет их выборочной миграцией обратно в популяции на вычислительных узлах. Работа мультипопуляционного алгоритма обсуждается на тестовых структурах с 9-10 независимыми атомамиA multipopulation genetic algorithm for a crystal structure solution from the X-ray powder diffraction data is proposed. Individual genetic algorithms are executed on different units of the computing cluster. The local optimization is performed periodically by the full-profile structure analysis (Rietveld method). The best trial structures are accumulated on the control unit for migration back to the routine compute units. The work of multi-population algorithm is discussed on 3 example of test structures with 9-10 independent atoms. The reliability of the structure search increases in a half order of magnitude more due to migratio

Possibilities of Neural Network Powder Diffraction Analysis Crystal Structure of Chemical Compounds

Some possibilities of using convolutional artificial neural networks (ANN) for powder diffraction structural analysis of crystalline substances have been investigated. First, ANNs are used to classify crystalline systems and space groups according to calculated full-profile diffractograms calculated from the crystal structures of the ICSD database (2017 year). The ICSD database contains 192004 structures, of which 80% was used for in-depth network training, and 20% for independent testing of recognition accuracy. The accuracy of classification by a network of crystalline systems was 87.9%, and that of space groups was 77.2%. Secondly, the ANN is used for a similar classification of structural models generated by the stochastic genetic algorithm in the search processes for triclinic crystal structures of test compound K4SnO4 according to their full-profile diffraction patterns. The classification criterion was the entry of one or several atoms into their crystallographic positions in the structure of a substance. Independent deep network training was performed on 120 thousand structural models of the K4PbO4 triclinic structure generated in several runs of the genetic algorithm. The accuracy of the classification of K4SnO4 structural models exceeded 50%. The results show that deeply trained convolutional ANNs can be effective for classifying crystal structures according to the structural characteristics of their powder diffraction patterns

Modeling of the Crystal Structure of Platinum Metal’s Complex Compounds by Using Parallel Computing Based on Genetic Algorithms and X-ray Diffraction Data

Модели кристаллической структуры комплексных соединений [Pd(CH3NH2)4][PdBr4] (пр.гр. P4/mnc (128), a=10.6866(7) Å, c=6.7262(3) Å, V=768.16(10) Å3) и [Pt(NH3)5Cl]Br3 (пр. гр. I41/a (88), параметры ячейки a=17.2587(5) Å; c=15.1164(3) Å, V=4502,61(10) Å3) определены с помощью разработанного мультипопуляционного параллельного генетического алгоритма (МПГА) и данных рентгеновской порошковой дифракции. Обсуждаются методика и результаты структурного анализа этих соединений по МПГАCrystal structure models of complex compounds [Pd(CH3NH2)4][PdBr4] (sp. gr. P4/mnc (128), a=10.6866(7) Å, c=6.7262(3) Å, V=768.16(10) Å3) and [Pt(NH3)5Cl]Br3 (sp. gr. I41/a (88), a=17.2587(5) Å; c=15.1164(3) Å, V=4502,61(10) Å3) has been determined by using the developed multipopulational parallel genetic algorithm (MPGA) and x-ray powder diffraction data. This paper presents the methodology and results of the structural analysis of these compounds obtained by application of the MPG

Multipopulation Genetic Algorithm for Simulation of the Crystal Structure from X-Ray Diffraction Data

Предложен мультипопуляционный параллельный генетический алгоритм для моделирования атомной кристаллической структуры химических соединений из данных рентгеновской порошковой дифракции. Индивидуальные генетические алгоритмы выполняются на разных узлах вычислительного кластера. Лучшие структурные модели из всех узлов подвергаются локальной оптимизации по методу полнопрофильного структурного анализа и накапливаются на управляющем узле. Он управляет их выборочной миграцией обратно в популяции на вычислительных узлах. Работа мультипопуляционного алгоритма обсуждается на тестовых структурах с 9-10 независимыми атомамиA multipopulation genetic algorithm for a crystal structure solution from the X-ray powder diffraction data is proposed. Individual genetic algorithms are executed on different units of the computing cluster. The local optimization is performed periodically by the full-profile structure analysis (Rietveld method). The best trial structures are accumulated on the control unit for migration back to the routine compute units. The work of multi-population algorithm is discussed on 3 example of test structures with 9-10 independent atoms. The reliability of the structure search increases in a half order of magnitude more due to migratio

[Pb2F2](SeO4): a heavier analogue of grandreefite, the first layered fluoride selenate

Текст статьи не публикуется в открытом доступе в соответствии с политикой журнала.Co-precipitation of PbF 2 and PbSeO 4 in weakly acidic media results in the formation of [Pb 2 F 2 ](SeO 4 ), the selenate analogue of the naturally occurring mineral grandreefite, [Pb 2 F 2 ](SO 4 ). The new compound is monoclinic, C2/c, a = 14.0784(2) Å, b = 4.6267(1) Å, c = 8.8628(1) Å, β = 108.98(1)°, V = 545.93(1) Å 3 . Its structure has been refined from powder data to R B = 1.55%. From thermal studies, it is established that the compound is stable in air up to about 300 °C, after which it gradually converts into a single phase with composition [Pb 2 O](SeO 4 ), space group C2/m, and lattice parameters a = 14.0332(1) Å, b = 5.7532(1) Å, c = 7.2113(1) Å, β = 115.07(1)°, V = 527.37(1) Å 3 . It is the selenate analogue of lanarkite, [Pb 2 O](SO 4 ), and phoenicochroite, [Pb 2 O](CrO 4 ), and its crystal structure was refined to R B = 1.21%. The formation of a single decomposition product upon heating in air suggests that this happens by a thermal hydrolysis mechanism, i.e., Pb 2 F 2 SeO 4 + H 2 O (vapor) → Pb 2 OSeO 4 + 2HF↑

[Pb2F2](SeO4): a heavier analogue of grandreefite, the first layered fluoride selenate

Текст статьи не публикуется в открытом доступе в соответствии с политикой журнала.Co-precipitation of PbF 2 and PbSeO 4 in weakly acidic media results in the formation of [Pb 2 F 2 ](SeO 4 ), the selenate analogue of the naturally occurring mineral grandreefite, [Pb 2 F 2 ](SO 4 ). The new compound is monoclinic, C2/c, a = 14.0784(2) Å, b = 4.6267(1) Å, c = 8.8628(1) Å, β = 108.98(1)°, V = 545.93(1) Å 3 . Its structure has been refined from powder data to R B = 1.55%. From thermal studies, it is established that the compound is stable in air up to about 300 °C, after which it gradually converts into a single phase with composition [Pb 2 O](SeO 4 ), space group C2/m, and lattice parameters a = 14.0332(1) Å, b = 5.7532(1) Å, c = 7.2113(1) Å, β = 115.07(1)°, V = 527.37(1) Å 3 . It is the selenate analogue of lanarkite, [Pb 2 O](SO 4 ), and phoenicochroite, [Pb 2 O](CrO 4 ), and its crystal structure was refined to R B = 1.21%. The formation of a single decomposition product upon heating in air suggests that this happens by a thermal hydrolysis mechanism, i.e., Pb 2 F 2 SeO 4 + H 2 O (vapor) → Pb 2 OSeO 4 + 2HF↑

Pb6O5(NO3)2: A Nonlinear Optical Oxynitrate Structurally Based on Lead Oxide Framework

Текст статьи не публикуется в открытом доступе в соответствии с политикой журнала.A high second harmonic generation response is demonstrated by a Pb6O5(NO3)2 lead oxynitrate whose identity was verified upon reinvestigation of the PbO–Pb(NO3)2 system. Its crystal structure exhibits a unique cationic [Pb6O5]2+ framework hosting orientationally ordered NO3– triangles in the channels. Easy preparation and high thermal stability (until ∼500 °C in air) suggest it to be a new promising NLO material

Possibilities of Neural Network Powder Diffraction Analysis Crystal Structure of Chemical Compounds

Исследованы некоторые возможности применения сверточных искусственных нейронных сетей (ИНС) для порошкового дифракционного структурного анализа кристаллических веществ. Во-первых, ИНС применены для классификации кристаллических систем и пространственных групп симметрии по расчетным полнопрофильным дифрактограммам, вычисленным из кристаллических структур базы данных ICSD 2017 г. База ICSD содержит 192004 структуры, из которых 80 % использовалось для глубокого обучения сети, а 20 % для независимого тестирования точности распознавания. Точность классификации сетью кристаллических систем составила 87,9 %, а пространственных групп – 77,2 %. Во- вторых, другая ИНС применена для классификации структурных моделей, сгенерированных стохастическим генетическим алгоритмом в процессах поиска кристаллических структур тестовых триклинных соединений K4SnO4 и K4SnO4, по их полнопрофильным дифрактограммам. Было сгенерировано около 150 тысяч структурных моделей каждой из этих структур. Глубокое обучение сети выполнялось на дифрактограммах структурных моделей K4PbO4. Обученная сеть была применена для классификации структурных моделей K4SnO4 по их дифрактограммам. Критерием классификации являлось попадание атомов в их кристаллографические позиции в структуре. Точность классификации адекватных позиций атомов в структурных моделях K4SnO4 превысила 50 %.Some possibilities of using convolutional artificial neural networks (ANN) for powder diffraction structural analysis of crystalline substances have been investigated. First, ANNs are used to classify crystalline systems and space groups according to calculated full-profile diffractograms calculated from the crystal structures of the ICSD database (2017 year). The ICSD database contains 192004 structures, of which 80% was used for in-depth network training, and 20% for independent testing of recognition accuracy. The accuracy of classification by a network of crystalline systems was 87.9%, and that of space groups was 77.2%. Secondly, the ANN is used for a similar classification of structural models generated by the stochastic genetic algorithm in the search processes for triclinic crystal structures of test compound K4SnO4 according to their full-profile diffraction patterns. The classification criterion was the entry of one or several atoms into their crystallographic positions in the structure of a substance. Independent deep network training was performed on 120 thousand structural models of the K4PbO4 triclinic structure generated in several runs of the genetic algorithm. The accuracy of the classification of K4SnO4 structural models exceeded 50%. The results show that deeply trained convolutional ANNs can be effective for classifying crystal structures according to the structural characteristics of their powder diffraction pattern

Synthesis, crystal structure, spectroscopic properties, and thermal behavior of rare-earth oxide selenates, Ln2O2SeO4 (Ln = La, Pr, Nd): The new perspectives of solid-state double-exchange synthesis

Текст статьи не публикуется в открытом доступе в соответствии с политикой журнала.Three rare-earth oxide selenates Ln2O2SeO4 (Ln = La, Pr, Nd) have been prepared via double-exchange solid-state reactions between respective LnOCl oxyhalides and potassium selenate. This approach succeeded to obtain singlephase specimens of La2O2SeO4 and Nd2O2SeO4, previously known as transients upon thermal decomposition of the corresponding selenates, as well as a new compound Pr2O2SeO4. Refinement of their crystal structures from powder X-ray diffraction data confirmed previous attributions to the grandreefite (Pb2F2SO4) structure type observed also for the Ln2O2SO4 oxide sulfates. According to polythermic X-ray studies, La2O2SeO4 is stable until at least 700 C. All compounds were characterized by infrared and X-ray photoelectron spectroscopy

Modeling of the Crystal Structure of Platinum Metal’s Complex Compounds by Using Parallel Computing Based on Genetic Algorithms and X-ray Diffraction Data

Модели кристаллической структуры комплексных соединений [Pd(CH3NH2)4][PdBr4] (пр.гр. P4/mnc (128), a=10.6866(7) Å, c=6.7262(3) Å, V=768.16(10) Å3) и [Pt(NH3)5Cl]Br3 (пр. гр. I41/a (88), параметры ячейки a=17.2587(5) Å; c=15.1164(3) Å, V=4502,61(10) Å3) определены с помощью разработанного мультипопуляционного параллельного генетического алгоритма (МПГА) и данных рентгеновской порошковой дифракции. Обсуждаются методика и результаты структурного анализа этих соединений по МПГАCrystal structure models of complex compounds [Pd(CH3NH2)4][PdBr4] (sp. gr. P4/mnc (128), a=10.6866(7) Å, c=6.7262(3) Å, V=768.16(10) Å3) and [Pt(NH3)5Cl]Br3 (sp. gr. I41/a (88), a=17.2587(5) Å; c=15.1164(3) Å, V=4502,61(10) Å3) has been determined by using the developed multipopulational parallel genetic algorithm (MPGA) and x-ray powder diffraction data. This paper presents the methodology and results of the structural analysis of these compounds obtained by application of the MPG