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Found 6 papers, 4 papers with code
Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms
However, training directly on simulated diffractograms from databases such as the ICSD is challenging due to its limited size, class-inhomogeneity, and bias toward certain structure types.
Connectivity Optimized Nested Graph Networks for Crystal Structures
Graph neural networks (GNNs) have been applied to a large variety of applications in materials science and chemistry.
MEGAN: Multi-Explanation Graph Attention Network
Unlike existing graph explainability methods, our network can produce node and edge attributional explanations along multiple channels, the number of which is independent of task specifications.
Graph neural networks for materials science and chemistry
Machine learning plays an increasingly important role in many areas of chemistry and materials science, e. g. to predict materials properties, to accelerate simulations, to design new materials, and to predict synthesis routes of new materials.
Implementing graph neural networks with TensorFlow-Keras
Graph neural networks are a versatile machine learning architecture that received a lot of attention recently.
Analyzing dynamical disorder for charge transport in organic semiconductors via machine learning
Organic semiconductors are indispensable for today's display technologies in form of organic light emitting diodes (OLEDs) and further optoelectronic applications.
