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Information × Registration Number 2120U007882, Article popup.category Препринт Title Predicting Properties of Crystals (AI translated) popup.author Lapchevskyi KostiantynLapchevskyi Kostiantyn popup.publication 01-01-2020 popup.source_user Український католицький університет popup.source https://hdl.handle.net/20.500.14570/2242 popup.publisher Description Crystalline structures are vital to the modern technology. Yet, we are still only starting to figure out how to properly estimate their directional properties using machine learning techniques. In order to improvethat, I build uponthe theory and codebase of Euclidean Neural Networks (networks equivariant to 3D rotations). The main contributions of this work are: a derivation of the decompositon/reconstruction equations of elastic tensor that enables using it as a train target, optimized CUDA implementation of the core operation PeriodicConvolution that makes it fast and scalable, and ananalys is of the trends of geometric structures and electronic properties of the crystal in Materials Project Database and how these trends impact hyper-parameters for convolutional neural network architectures such as Euclidean Neural Networks. popup.nrat_date 2025-11-05 Close