Themed collection Data Driven Crystal Engineering

11 items
Communication

Theoretical insight into the relevance between the oxidation states of CeO2 supported Pt4+/2+/1+/0/2− and their HER performance

The CeO2 supported electron-enriched Pt2− is more suitable for HER than Pt0 and Ptδ+.

Graphical abstract: Theoretical insight into the relevance between the oxidation states of CeO2 supported Pt4+/2+/1+/0/2− and their HER performance
From the themed collection: Data Driven Crystal Engineering
Paper

Predictive nonlinear optical crystal formation energy regression model based on convolutional neural networks

A convolutional neural network (CNN) model has been constructed to predict the formation energy of nonlinear optical crystals solely based on their chemical formulas.

Graphical abstract: Predictive nonlinear optical crystal formation energy regression model based on convolutional neural networks
From the themed collection: Data Driven Crystal Engineering
Paper

Toward predicting surface energy of rutile TiO2 with machine learning

A database of rutile TiO2 containing 3000 morphologies was established. With this database, the surface energy was predicted from the experimentally observed crystal equilibrium morphology using the KNN model.

Graphical abstract: Toward predicting surface energy of rutile TiO2 with machine learning
Open Access Paper

Predicting pharmaceutical crystal morphology using artificial intelligence

We present the use of artificial intelligence to predict the morphology of crystallizing active pharmaceutical ingredients, first using publicly available data, and then using our own screening efforts to address the limitations we identified.

Graphical abstract: Predicting pharmaceutical crystal morphology using artificial intelligence
Paper

A study to discover novel pharmaceutical cocrystals of pelubiprofen with a machine learning approach compared

Pharmaceutical cocrystals of pelubiprofen (PF) were discovered for the first time. 16 candidates to form cocrystals with PF were selected via the ANN model and the pKa rule.

Graphical abstract: A study to discover novel pharmaceutical cocrystals of pelubiprofen with a machine learning approach compared
Paper

Importance of raw material features for the prediction of flux growth of Al2O3 crystals using machine learning

We evaluated the role of raw-material features for machine-learning prediction of the flux crystal growth of Al2O3 in MoO3 based on 185 types of growth trials.

Graphical abstract: Importance of raw material features for the prediction of flux growth of Al2O3 crystals using machine learning
Paper

Virtual coformer screening by a combined machine learning and physics-based approach

Cocrystals as a solid form technology for improving physicochemical properties have gained increasing popularity in the pharmaceutical, nutraceutical, and agrochemical industries.

Graphical abstract: Virtual coformer screening by a combined machine learning and physics-based approach
From the themed collection: Data Driven Crystal Engineering
Paper

Actuation performance of a photo-bending crystal modeled by machine learning-based regression

The bending deflection and blocking force of photo-bending crystals of different sizes were experimentally measured at various light intensities, and then modeled by the machine learning-based regression.

Graphical abstract: Actuation performance of a photo-bending crystal modeled by machine learning-based regression
From the themed collection: Data Driven Crystal Engineering
Paper

Geometrical design of a crystal growth system guided by a machine learning algorithm

This study proposes a new high-speed method for designing crystal growth systems. It is capable of optimizing large numbers of parameters simultaneously which is difficult for traditional experimental and computational techniques.

Graphical abstract: Geometrical design of a crystal growth system guided by a machine learning algorithm
From the themed collection: Data Driven Crystal Engineering
Paper

Adaptive process control for crystal growth using machine learning for high-speed prediction: application to SiC solution growth

A time-dependent recipe designed by an adaptive control method can consistently maintain the optimal growth conditions despite the unsteady growth environment.

Graphical abstract: Adaptive process control for crystal growth using machine learning for high-speed prediction: application to SiC solution growth
From the themed collection: Data Driven Crystal Engineering
Paper

Evaluation of focused beam reflectance measurement (FBRM) for monitoring and predicting the crystal size of carbamazepine in crystallization processes

Pharmaceutical crystallization affects the properties of APIs as it determines the purity and crystal size distribution, among other attributes. This work presents two CLD–CSD models, theoretical and empirical, for a model compound.

Graphical abstract: Evaluation of focused beam reflectance measurement (FBRM) for monitoring and predicting the crystal size of carbamazepine in crystallization processes
From the themed collection: Data Driven Crystal Engineering
11 items

About this collection

Artificial intelligence technologies, large-scale tag data and improvement of computing performance warrant opportunities for intelligent analysis of crystal engineering big data. Machine learning can well learn and extract the crystal engineering characteristics of complex data and help to promote the generation of new mechanisms and new knowledge. For this, it is important to develop new models and methodologies to address big crystal engineering data challenges. This themed collection, guest edited by Professor Dongfeng Xue and Dr Haitao Zhao (Multiscale Crystal Materials Research Centre, Shenzhen Institute of Advanced Technology, CAS, China) aims to develop the ‘Fourth Paradigm’; revolutionizing crystalline materials R&D methods using advanced data-driven approaches to crystalline materials discovery with articles that show the significant potential of interdisciplinary research into data-driven discovery and digital manufacturing of crystalline materials.

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