Automated prediction of lattice parameters from X-ray powder diffraction patterns

A key step in the analysis of powder X-ray diffraction (PXRD) data is the accurate determination of unit-cell lattice parameters. This step often requires significant human intervention and is a bottleneck that hinders efforts towards automated analysis. This work develops a series of one-dimensional convolutional neural networks (1D-CNNs) trained to provide lattice parameter estimates for each crystal system. A mean absolute percentage error of approximately 10% is achieved for each crystal system, which corresponds to a 100- to 1000-fold reduction in lattice parameter search space volume. The models learn from nearly one million crystal structures contained within the Inorganic Crystal Structure Database and the Cambridge Structural Database and, due to the nature of these two complimentary databases, the models generalize well across chemistries. A key component of this work is a systematic analysis of the effect of different realistic experimental non-idealities on model performance. It is found that the addition of impurity phases, baseline noise and peak broadening present the greatest challenges to learning, while zero-offset error and random intensity modulations have little effect. However, appropriate data modification schemes can be used to bolster model performance and yield reasonable predictions, even for data which simulate realistic experimental non-idealities. In order to obtain accurate results, a new approach is introduced which uses the initial machine learning estimates with existing iterative whole-pattern refinement schemes to tackle automated unit-cell solution.

Pengaruh lama maturasi pada sintesis biokeramik komposit CaO-TiO2 terhadap ukuran kristal, mikrostruktur dan kekerasan

Perkembangan Ilmu Pengetahuan dan Teknologi (IPTEK) akhir-akhir ini mendorong banyaknya inovasi dalam dunia medis terutama penggunaan biomaterial sebagai implan pengganti tulang dan gigi, salah satunya bahan tersebut adalah biokeramik komposit CaO-TiO2. Bahan biokeramik komposit CaO-TiO2 dapat digunakan untuk memperbaiki bagian tubuh yang rusak terutama sebagai implan gigi, penyambung tulang, struktur penahan katup jantung, dan pengganti tulang tengkorak. Paduan antara CaO-TiO2 memiliki beberapa keuntungan diantaranya memiliki biokompatibilitas yang baik, dapat tumbuh serta berkembang bersama-sama dengan tulang asli serta memiliki ketahanan mekanik yang baik. Berdasarkan paparan di atas, tujuan dari penelitian ini adalah mengetahui pengaruh lama maturasi pada biokeramik komposit CaO-TiO2 dengan metode kopresipitasi terhadap kristalinitas, mikrostruktur, dan kekerasan. Pada penelitian ini bahan dasar yang digunakan adalah CaO yang berasal dari batuan kapur alam yang diambil dari pantai Balekambang Kabupaten Malang dan TiO2 dengan kemurnian 99 persen. Sampel dilarutkan dalam aquades dan distirer selama 15 jam pada suhu 70 derajat celcius. Lama maturasi divariasi mulai dari 12, 24, 36, 48, dan 60 jam, dianneling pada suhu 100 derajat celcius selama 24 jam dan disintering selama 4 jam pada suhu 1100 derajat celcius. Sampel dikarakterisasi ukuran kristal, mikrostruktur, dan kekerasan, dengan menggunakan XRD, SEM, dan Micro Vickers Hardness. Hasil analisis CaO-TiO2 menunjukkan kecocokan dan keberhasilan sintesis dengan model pembanding CaO-TiO2 dari Inorganic Crystal Structure Database (ICSD) dengan nilai score diatas 50. Berdasarkan perhitungan teoritik yang dilakukan dengan menentukan nilai FWHM (Full Widht at Half Maximum) dari pola difraksi sampel yang kemudian digunakan pada formula scherrer, diperoleh hasil peningkatan ukuran kristal yang bervariasi terhadap lama maturasi komposit CaO-TiO2 dengan besar antara 45,06 nm-70,85 nm. Dengan meningkatnya ukuran kristal terhadap lama maturasi maka akan disertai oleh peningkatan ukuran butir, sehingga semakin sedikit jumlah pori-pori yang terbentuk pada bahan yang ditunjukan oleh menurunnya nilai luas fraksi pori sebesar 4,97 persen pada lama maturasi 12 jam menjadi 4,79 persen pada lama maturasi 60 jam. Dengan semakin kecilnya nilai fraksi total pori maka semakin besar kekerasan dari bahan tersebut, hal ini ditunjukan dengan nilai kekerasan tertinggi diperoleh pada lama maturasi 60 jam sebesar 497,2 MPa.

Crystal structure of silver carbonate iodide Ag10(CO3)3I4

The title silver carbonate iodide, Ag10(CO3)3I4, decasilver(I) tris(carbonate) tetraiodide, was recently reported as a precursor of the new superionic conductor Ag17(CO3)3I11. Ag10(CO3)3I4, was prepared by heating a stoichiometric powder mixture of AgI and Ag2CO3 at 430 K. A single-crystal suitable for X-ray diffraction analysis was obtained by slow cooling of a melt with an AgI-rich composition down from 453 K. Ag10(CO3)3I4 exhibits a layered crystal structure packed along [10\overline{1}], in which Ag atoms are intercalated between the layers of hexagonally close-packed I atoms, and CO3 groups. Up to now, Cs3Pb2(CO3)3I is the only other compound containing carbonate groups and iodide ions registered in the Inorganic Crystal Structure Database.

High-throughput computational screening for two-dimensional magnetic materials based on experimental databases of three-dimensional compounds

We perform a computational screening for two-dimensional (2D) magnetic materials based on experimental bulk compounds present in the Inorganic Crystal Structure Database and Crystallography Open Database. A recently proposed geometric descriptor is used to extract materials that are exfoliable into 2D derivatives and we find 85 ferromagnetic and 61 antiferromagnetic materials for which we obtain magnetic exchange and anisotropy parameters using density functional theory. For the easy-axis ferromagnetic insulators we calculate the Curie temperature based on a fit to classical Monte Carlo simulations of anisotropic Heisenberg models. We find good agreement with the experimentally reported Curie temperatures of known 2D ferromagnets and identify 10 potentially exfoliable 2D ferromagnets that have not been reported previously. In addition, we find 18 easy-axis antiferromagnetic insulators with several compounds exhibiting very strong exchange coupling and magnetic anisotropy.

The Earth Mover’s Distance as a Metric for the Space of Inorganic Compositions

It is a core problem in any field to reliably tell how close two objects are to being the same, and once this relation has been established we can use this information to precisely quantify potential relationships, both analytically and with machine learning (ML). For inorganic solids, the chemical composition is a fundamental descriptor, which can be represented by assigning the ratio of each element in the material to a vector. These vectors are a convenient mathematical data structure for measuring similarity, but unfortunately, the standard metric (the Euclidean distance) gives little to no variance in the resultant distances between chemically dissimilar compositions. We present the Earth Mover’s Distance (EMD) for inorganic compositions, a well-defined metric which enables the measure of chemical similarity in an explainable fashion. We compute the EMD between two compositions from the ratio of each of the elements and the absolute distance between the elements on the modified Pettifor scale. This simple metric shows clear strength at distinguishing compounds and is efficient to compute in practice. The resultant distances have greater alignment with chemical understanding than the Euclidean distance, which is demonstrated on the binary compositions of the Inorganic Crystal Structure Database (ICSD). The EMD is a reliable numeric measure of chemical similarity that can be incorporated into automated workflows for a range of ML techniques. We have found that with no supervision the use of this metric gives a distinct partitioning of binary compounds into clear trends and families of chemical property, with future applications for nearest neighbor search queries in chemical database retrieval systems and supervised ML techniques.

The Earth Mover’s Distance as a Metric for the Space of Inorganic Compositions

It is a core problem in any field to reliably tell how close two objects are to being the same, and once this relation has been established we can use this information to precisely quantify potential relationships, both analytically and with machine learning (ML). For inorganic solids, the chemical composition is a fundamental descriptor, which can be represented by assigning the ratio of each element in the material to a vector. These vectors are a convenient mathematical data structure for measuring similarity, but unfortunately, the standard metric (the Euclidean distance) gives little to no variance in the resultant distances between chemically dissimilar compositions. We present the Earth Mover’s Distance (EMD) for inorganic compositions, a well-defined metric which enables the measure of chemical similarity in an explainable fashion. We compute the EMD between two compositions from the ratio of each of the elements and the absolute distance between the elements on the modified Pettifor scale. This simple metric shows clear strength at distinguishing compounds and is efficient to compute in practice. The resultant distances have greater alignment with chemical understanding than the Euclidean distance, which is demonstrated on the binary compositions of the Inorganic Crystal Structure Database (ICSD). The EMD is a reliable numeric measure of chemical similarity that can be incorporated into automated workflows for a range of ML techniques. We have found that with no supervision the use of this metric gives a distinct partitioning of binary compounds into clear trends and families of chemical property, with future applications for nearest neighbor search queries in chemical database retrieval systems and supervised ML techniques.

Noble Gas Bonding Interactions Involving Xenon Oxides and Fluorides

Noble gas (or aerogen) bond (NgB) can be outlined as the attractive interaction between an electron-rich atom or group of atoms and any element of Group-18 acting as an electron acceptor. The IUPAC already recommended systematic nomenclature for the interactions of groups 17 and 16 (halogen and chalcogen bonds, respectively). Investigations dealing with noncovalent interactions involving main group elements (acting as Lewis acids) have rapidly grown in recent years. They are becoming acting players in essential fields such as crystal engineering, supramolecular chemistry, and catalysis. For obvious reasons, the works devoted to the study of noncovalent Ng-bonding interactions are significantly less abundant than halogen, chalcogen, pnictogen, and tetrel bonding. Nevertheless, in this short review, relevant theoretical and experimental investigations on noncovalent interactions involving Xenon are emphasized. Several theoretical works have described the physical nature of NgB and their interplay with other noncovalent interactions, which are discussed herein. Moreover, exploring the Cambridge Structural Database (CSD) and Inorganic Crystal Structure Database (ICSD), it is demonstrated that NgB interactions are crucial in governing the X-ray packing of xenon derivatives. Concretely, special attention is given to xenon fluorides and xenon oxides, since they exhibit a strong tendency to establish NgBs.

Geometric Analysis and Formability of the Cubic A2BX6 Vacancy Ordered Double Perovskite Structure

<p>A geometric analysis of the cubic A₂BX₆ structure commonly formed by metal halides is presented. Using the ‘hard sphere’ approximation, where the ions are represented by spheres of a fixed radius, we derive four limiting models that each constrain the distances between constituent ions in different ways. We compare the lattice parameters predicted by these four models with experimental data from the Inorganic Crystal Structure Database (ICSD). For the fluorides, the maintenance of the AX bond length at the sum of the A and X radii gives the best approximation of the lattice parameter, leading to structures with widely separated BX₆ octahedra. For the heavier halides, a balance between forming an A site cavity of the correct size, and maintaining suitable anion-anion distances determines the lattice parameter. It is found that in many A₂BX₆ compounds of heavier halides, the neighbouring octahedra show very significant anion-anion overlap, meaning that the commonly used description of these materials of having isolated BX₆ octahedra is misleading. We use the geometric models to derive formability criteria for vacancy ordered double perovskites. </p>

Geometric Analysis and Formability of the Cubic A2BX6 Vacancy Ordered Double Perovskite Structure

<p>A geometric analysis of the cubic A₂BX₆ structure commonly formed by metal halides is presented. Using the ‘hard sphere’ approximation, where the ions are represented by spheres of a fixed radius, we derive four limiting models that each constrain the distances between constituent ions in different ways. We compare the lattice parameters predicted by these four models with experimental data from the Inorganic Crystal Structure Database (ICSD). For the fluorides, the maintenance of the AX bond length at the sum of the A and X radii gives the best approximation of the lattice parameter, leading to structures with widely separated BX₆ octahedra. For the heavier halides, a balance between forming an A site cavity of the correct size, and maintaining suitable anion-anion distances determines the lattice parameter. It is found that in many A₂BX₆ compounds of heavier halides, the neighbouring octahedra show very significant anion-anion overlap, meaning that the commonly used description of these materials of having isolated BX₆ octahedra is misleading. We use the geometric models to derive formability criteria for vacancy ordered double perovskites. </p>

High-Throughput Computational Search for Half-Metallic Oxides

Half metals are a peculiar class of ferromagnets that have a metallic density of states at the Fermi level in one spin channel and simultaneous semiconducting or insulating properties in the opposite one. Even though they are very desirable for spintronics applications, identification of robust half-metallic materials is by no means an easy task. Because their unusual electronic structures emerge from subtleties in the hybridization of the orbitals, there is no simple rule which permits to select a priori suitable candidate materials. Here, we have conducted a high-throughput computational search for half-metallic compounds. The analysis of calculated electronic properties of thousands of materials from the inorganic crystal structure database allowed us to identify potential half metals. Remarkably, we have found over two-hundred strong half-metallic oxides; several of them have never been reported before. Considering the fact that oxides represent an important class of prospective spintronics materials, we have discussed them in further detail. In particular, they have been classified in different families based on the number of elements, structural formula, and distribution of density of states in the spin channels. We are convinced that such a framework can help to design rules for the exploration of a vaster chemical space and enable the discovery of novel half-metallic oxides with properties on demand.