Molecular mechanics modelling of porphyrins. Using artificial neural networks to develop metal parameters for four-coordinate metalloporphyrinsElectronic supplementary information (ESI) available: Molecular mechanics parameters, comparisons between crystallographic and molecular mechanics geometries, error response surfaces, and crystal structures. See http://www.rsc.org/suppdata/cp/b2/b203360g/

In materials science, crystal structures are the cornerstone in the structure–property paradigm. The description of crystal compounds may be ascribed to the number of different atomic chemical environments, which are related to the Wyckoff sites. Hence, a set of features related to the different atomic environments in a crystal compound can be constructed as input data for artificial neural networks (ANNs). In this article, we show the performance of a series of ANNs developed using crystal-site-based features. These ANNs were developed to classify compounds into halite, garnet, fluorite, hexagonal perovskite, ilmenite, layered perovskite, -o-tp- perovskite, perovskite, and spinel structures. Using crystal-site-based features, the ANNs were able to classify the crystal compounds with a 93.72% average precision. Furthermore, the ANNs were able to retrieve missing compounds with one of these archetypical structure types from a database. Finally, we showed that the developed ANNs were also suitable for a multitask learning paradigm, since the extracted information in the hidden layers linearly correlated with lattice parameters of the crystal structures.

Download Full-text

Neural network investigation of borate crystal properties

Zeitschrift für Kristallographie - Crystalline Materials ◽

10.1524/zkri.217.5.205.20634 ◽

2002 ◽

Vol 217 (5) ◽

Cited By ~ 3

Author(s):

M. J. Barber ◽

P. Becker

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Crystal Structures ◽

Chemical Properties ◽

Crystal Chemical ◽

Borate Crystal ◽

Crystal Properties ◽

Artificial Neural

AbstractCorrelations between crystal chemical properties of anhydrous oxoborate crystals wereanalysed using artificial neural networks. Using structuralproperties of oxoborate crystal structures described inthe literature, we developed several neural network modelsthat capture statistical relations between crystalchemical properties of the anhydrous oxoborates from the existingdata sets. This indicates the suitability of neural networks forthe prediction of structural propertiesof crystals.

Download Full-text

Development of artificial neural networks based confidence intervals and response surfaces for the optimization of coagulation performance

Water Science & Technology Water Supply ◽

10.2166/ws.2015.066 ◽

2015 ◽

Vol 15 (5) ◽

pp. 1079-1087 ◽

Cited By ~ 1

Author(s):

Robert H. McArthur ◽

Robert C. Andrews

Keyword(s):

Carbon Dioxide ◽

Neural Networks ◽

Artificial Neural Networks ◽

Water Treatment ◽

Confidence Intervals ◽

Response Surfaces ◽

Average Interval ◽

Squared Error ◽

Interval Width ◽

Artificial Neural

Effective coagulation is essential to achieving drinking water treatment objectives when considering surface water. To minimize settled water turbidity, artificial neural networks (ANNs) have been adopted to predict optimum alum and carbon dioxide dosages at the Elgin Area Water Treatment Plant. ANNs were applied to predict both optimum carbon dioxide and alum dosages with correlation (R2) values of 0.68 and 0.90, respectively. ANNs were also used to developed surface response plots to ease optimum selection of dosage. Trained ANNs were used to predict turbidity outcomes for a range of alum and carbon dioxide dosages and these were compared to historical data. Point-wise confidence intervals were obtained based on error and squared error values during the training process. The probability of the true value falling within the predicted interval ranged from 0.25 to 0.81 and the average interval width ranged from 0.15 to 0.62 NTU. Training an ANN using the squared error produced a larger average interval width, but better probability of a true prediction interval.

Download Full-text