scholarly journals Analogical discovery of disordered perovskite oxides by crystal structure information hidden in unsupervised material fingerprints

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Achintha Ihalage ◽  
Yang Hao
Keyword(s):  
Crystal Structure ◽  
Web Mining ◽  
Learning Strategy ◽  
Computational Modelling ◽  
Search Space ◽  
Supervised Machine Learning ◽  
Disordered Materials ◽  
Structure Information ◽  
Unsupervised Deep Learning ◽  
Analytical Complexity

AbstractCompositional disorder induces myriad captivating phenomena in perovskites. Target-driven discovery of perovskite solid solutions has been a great challenge due to the analytical complexity introduced by disorder. Here, we demonstrate that an unsupervised deep learning strategy can find fingerprints of disordered materials that embed perovskite formability and underlying crystal structure information by learning only from the chemical composition, manifested in $$({{\rm{A}}}_{1-{\rm{x}}}{{\rm{A}}^{\prime} }_{{\rm{x}}}){{\rm{BO}}}_{3}$$ ( A 1 − x A ′ x ) BO 3 and $${\rm{A}}({{\rm{B}}}_{1-{\rm{x}}}{{\rm{B}}^{\prime} }_{{\rm{x}}}){{\rm{O}}}_{3}$$ A ( B 1 − x B ′ x ) O 3 formulae. This phenomenon can be capitalized to predict the crystal symmetry of experimental compositions, outperforming several supervised machine learning (ML) algorithms. The educated nature of material fingerprints has led to the conception of analogical materials discovery that facilitates inverse exploration of promising perovskites based on similarity investigation with known materials. The search space of unstudied perovskites is screened from ~600,000 feasible compounds using experimental data powered ML models and automated web mining tools at a 94% success rate. This concept further provides insights on possible phase transitions and computational modelling of complex compositions. The proposed quantitative analysis of materials analogies is expected to bridge the gap between the existing materials literature and the undiscovered terrain.

scholarly journals VISUALISASI STRUKTUR KRISTAL KERAMIK PEROVSKITE MENGGUNAKAN VESTA

2018 ◽  
Vol 15 (1) ◽  
pp. 46
Author(s):  
Sundami Restiana ◽  
Ari Sulistyo Rini
Keyword(s):  
Crystal Structure ◽  
Diffraction Pattern ◽  
Critical Concentration ◽  
Ceramic Materials ◽  
Lattice Parameter ◽  
Peak Position ◽  
X Ray Diffraction ◽  
X Ray ◽  
Structure Information ◽  
Software Program

Visualization of crystal structures and simulation of X-ray diffraction patterns of perovskite ceramic was successfully performed by VESTA software programs. The purpose of this research is to obtain the relation of lattice parameter, and composition to the diffraction pattern. The software program produces crystal structure information and a representative X-ray diffraction pattern for the ceramic materials. The program needs several input parameters such as the coordinates of each constituent atom, lattice parameters, and space symmetry. The obtained output of the software program are in the form of diffraction pattern graph and crystal structure data which gives the description of the profile and type (phase) of ceramic material. The results showed that the peak position and intensity of the diffraction pattern are influenced by the arrangement of  the atoms within the unit cell. The addition of impurity atoms such as Sr on the Ba side in BaTiO3 causes the BaTiO3 structure changes from Orthorombic (a≠b≠c) to Tetragonal (a=b≠c) structure. Based on the simulation, it can be predicted that the critical concentration of the change of structure occur at Sr concentration about 0.4.

OChemDb: the free online Open Chemistry Database portal for searching and analysing crystal structure information

2018 ◽  
Vol 51 (4) ◽  
pp. 1229-1236 ◽  
Author(s):  
Angela Altomare ◽  
Nicola Corriero ◽  
Corrado Cuocci ◽  
Aurelia Falcicchio ◽  
Anna Moliterni ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Local Environment ◽  
Chemical Information ◽  
Continuous Growth ◽  
Structure Information ◽  
Space Groups ◽  
Online Portal ◽  
Great Utility ◽  
Structure Knowledge ◽  
Crystallography Open Database

The Open Chemistry Database (OChemDb) is a new free online portal which uses an appropriately designed database of already solved crystal structures. It makes freely available computational and graphical tools for searching and analysing crystal-chemical information of organic, metal–organic and inorganic structures, and providing statistics on desired bond distances, bond angles, torsion angles and space groups. Atom types have been classified by an identifier code containing information about the chemical topology and local environment. The crystallographic data used by OChemDb are acquired from the CIFs contained in the free small-molecule Crystallography Open Database (COD). OChemDb offers easy-to-use and intuitive options for searching. It is updated by following the continuous growth of information stored in the COD. It can be of great utility for structural chemistry, in particular in the process of determination of a new crystal structure, and for any discipline involving crystalline structure knowledge. The use of OChemDb requires only a web browser and an internet connection. Every device (mobile or desktop) and every operating system is able to use OChemDb by accessing its web page. Examples of application of OChemDb are reported.

scholarly journals Crystal structure of propionitrile (CH3CH2CN) determined using synchrotron powder X-ray diffraction

2020 ◽  
Vol 27 (1) ◽  
pp. 212-216
Author(s):  
Helen E. A. Brand ◽  
Qinfen Gu ◽  
Justin A. Kimpton ◽  
Rebecca Auchettl ◽  
Courtney Ennis
Keyword(s):  
Crystal Structure ◽  
Thermal Expansion ◽  
Solar System ◽  
Infrared Spectra ◽  
Lower Stratosphere ◽  
Computational Modelling ◽  
Outer Solar System ◽  
X Ray Diffraction ◽  
Near Surface ◽  
X Ray

The structure and thermal expansion of the astronomical molecule propionitrile have been determined from 100 to 150 K using synchrotron powder X-ray diffraction. This temperature range correlates with the conditions of Titan's lower stratosphere, and near surface, where propionitrile is thought to accumulate and condense into pure and mixed-nitrile phases. Propionitrile was determined to crystallize in space group, Pnma (No. 62), with unit cell a = 7.56183 (16) Å, b = 6.59134 (14) Å, c = 7.23629 (14), volume = 360.675 (13) Å3 at 100 K. The thermal expansion was found to be highly anisotropic with an eightfold increase in expansion between the c and b axes. These data will prove crucial in the computational modelling of propionitrile–ice systems in outer Solar System environments, allowing us to simulate and assign vibrational peaks in the infrared spectra for future use in planetary astronomy.

scholarly journals Data Association via Set Packing for Computer Vision Applications

2020 ◽  
Vol 2 (3) ◽  
pp. 167-191
Author(s):  
Julian Yarkony ◽  
Yossiri Adulyasak ◽  
Maneesh Singh ◽  
Guy Desaulniers
Keyword(s):  
Computer Vision ◽  
Column Generation ◽  
Data Association ◽  
Search Space ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Superior Performance ◽  
Set Packing ◽  
Unified Framework ◽  
Computer Vision Applications

Significant progress has been made in the field of computer vision because of the development of supervised machine learning algorithms, which efficiently extract information from high-dimensional data such as images and videos. Such techniques are particularly effective at recognizing the presence or absence of entities in the domains where labeled data are abundant. However, supervised learning is not sufficient in applications where one needs to annotate each unique entity in crowded scenes respecting known domain-specific structures of those entities. This problem, known as data association, provides fertile ground for the application of combinatorial optimization. In this review paper, we present a unified framework based on column generation for some computer vision applications, namely multiperson tracking, multiperson pose estimation, and multicell segmentation, which can be formulated as set packing problems with a massive number of variables. To solve them, column generation algorithms are applied to circumvent the need to enumerate all variables explicitly. To enhance the solution process, we provide a general approach for applying subset-row inequalities to tighten the formulations and introduce novel dual-optimal inequalities to reduce the dual search space. The proposed algorithms and their enhancements are successfully applied to solve the three aforementioned computer vision problems and achieve superior performance over benchmark approaches. The common framework presented allows us to leverage operations research methodologies to efficiently tackle computer vision problems.

MINING WEB USAGE GRAPHS USING EXAMPLE SEARCH SPACE

2002 ◽  
Vol 02 (02) ◽  
pp. 209-220 ◽  
Author(s):  
V. UMA MAHESWARI ◽  
A. SIROMONEY ◽  
K. M. MEHATA
Keyword(s):  
Web Mining ◽  
Learning Algorithm ◽  
Search Space ◽  
Web Data ◽  
Web Usage ◽  
Navigation Patterns ◽  
Example Space

Web mining refers to the process of discovering potentially useful and previously unknown information or knowledge from web data. A graph-based framework is used for classifying Web users based on their navigation patterns. GOLEM is a learning algorithm that uses the example space to restrict the solution search space. In this paper, this algorithm is modified for the graph-based framework. GOLEM is appropriate in this application where the solution search space is very large. An experimental illustration is presented.

scholarly journals Modulation of neural activity in frontopolar cortex drives reward-based motor learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
M. Herrojo Ruiz ◽  
T. Maudrich ◽  
B. Kalloch ◽  
D. Sammler ◽  
R. Kenville ◽  
...  
Keyword(s):  
Motor Learning ◽  
Learning Strategy ◽  
Computational Modelling ◽  
Performance Goal ◽  
Healthy Human ◽  
Frontopolar Cortex ◽  
Human Participants ◽  
Efficient Learning ◽  
The Right ◽  
Control Sham

AbstractThe frontopolar cortex (FPC) contributes to tracking the reward of alternative choices during decision making, as well as their reliability. Whether this FPC function extends to reward gradients associated with continuous movements during motor learning remains unknown. We used anodal transcranial direct current stimulation (tDCS) over the right FPC to investigate its role in reward-based motor learning. Nineteen healthy human participants practiced novel sequences of finger movements on a digital piano with corresponding auditory feedback. Their aim was to use trialwise reward feedback to discover a hidden performance goal along a continuous dimension: timing. We additionally modulated the contralateral motor cortex (left M1) activity, and included a control sham stimulation. Right FPC-tDCS led to faster learning compared to lM1-tDCS and sham through regulation of motor variability. Bayesian computational modelling revealed that in all stimulation protocols, an increase in the trialwise expectation of reward was followed by greater exploitation, as shown previously. Yet, this association was weaker in lM1-tDCS suggesting a less efficient learning strategy. The effects of frontopolar stimulation were dissociated from those induced by lM1-tDCS and sham, as motor exploration was more sensitive to inferred changes in the reward tendency (volatility). The findings suggest that rFPC-tDCS increases the sensitivity of motor exploration to updates in reward volatility, accelerating reward-based motor learning.

scholarly journals Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1737 ◽  
Author(s):  
Bahareh Kalantar ◽  
Naonori Ueda ◽  
Vahideh Saeidi ◽  
Kourosh Ahmadi ◽  
Alfian Abdul Halin ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Landslide Susceptibility ◽  
Learning Algorithm ◽  
Computational Modelling ◽  
Susceptibility Mapping ◽  
Boosted Regression Trees ◽  
Supervised Machine Learning ◽  
Landslide Susceptibility Mapping ◽  
Ensemble Model ◽  
Conditioning Factors

Predicting landslide occurrences can be difficult. However, failure to do so can be catastrophic, causing unwanted tragedies such as property damage, community displacement, and human casualties. Research into landslide susceptibility mapping (LSM) attempts to alleviate such catastrophes through the identification of landslide prone areas. Computational modelling techniques have been successful in related disaster scenarios, which motivate this work to explore such modelling for LSM. In this research, the potential of supervised machine learning and ensemble learning is investigated. Firstly, the Flexible Discriminant Analysis (FDA) supervised learning algorithm is trained for LSM and compared against other algorithms that have been widely used for the same purpose, namely Generalized Logistic Models (GLM), Boosted Regression Trees (BRT or GBM), and Random Forest (RF). Next, an ensemble model consisting of all four algorithms is implemented to examine possible performance improvements. The dataset used to train and test all the algorithms consists of a landslide inventory map of 227 landslide locations. From these sources, 13 conditioning factors are extracted to be used in the models. Experimental evaluations are made based on True Skill Statistic (TSS), the Receiver Operation characteristic (ROC) curve and kappa index. The results show that the best TSS (0.6986), ROC (0.904) and kappa (0.6915) were obtained by the ensemble model. FDA on its own seems effective at modelling landslide susceptibility from multiple data sources, with performance comparable to GLM. However, it slightly underperforms when compared to GBM (BRT) and RF. RF seems most capable compared to GBM, GLM, and FDA, when dealing with all conditioning factors.

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