Please use this identifier to cite or link to this item:
http://elar.urfu.ru/handle/10995/111123
Title: | Machine Learning Methods for Predicting the Lattice Characteristics of Materials |
Authors: | Filanovich, A. N. Povzner, A. A. |
Issue Date: | 2020 |
Publisher: | Institute of Electrical and Electronics Engineers Inc. IEEE |
Citation: | Filanovich A. N. Machine Learning Methods for Predicting the Lattice Characteristics of Materials / A. N. Filanovich, A. A. Povzner // Proceedings - 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2020. — 2020. — Vol. — P. 414-416. — 9117689. |
Abstract: | Data on 5244 crystalline compounds from the open AFLOWlib repository are used to build machine learning models, which enable to predict important features of phonon spectrum of a material (Debye temperature and Gruneisen parameter) required for simulation of its lattice properties. We build two types of descriptors: the first one contains data solely on the chemical composition of a compound and the second one incorporates information on the elemental properties of atoms that make up the compound and additionally contains several features regarding its crystal structure. The regression models are built using four popular approaches - gradient boosting (GB), random forests (RF), artificial neural networks (ANN) and support vector machines (SVM). Prior the regression a search for the best values of hyperparameters has been performed for each of the model supplemented with a 5-fold cross validation. We compare prediction accuracy of models based on different methods as well as trained on each of the descriptors. © 2020 IEEE. |
Keywords: | MACHINE LEARNING REGRESSION THERMAL PROPERTIES BIOMEDICAL ENGINEERING CRYSTAL ATOMIC STRUCTURE DECISION TREES FORECASTING NANOCRYSTALLINE MATERIALS PHONONS PREDICTIVE ANALYTICS SUPPORT VECTOR MACHINES SUPPORT VECTOR REGRESSION CHEMICAL COMPOSITIONS CRYSTALLINE COMPOUNDS GRADIENT BOOSTING GRUNEISEN PARAMETERS IMPORTANT FEATURES MACHINE LEARNING METHODS MACHINE LEARNING MODELS PREDICTION ACCURACY LEARNING SYSTEMS |
URI: | http://elar.urfu.ru/handle/10995/111123 |
Access: | info:eu-repo/semantics/openAccess |
Conference name: | 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2020 |
Conference date: | 14 May 2020 through 15 May 2020 |
SCOPUS ID: | 85089654270 |
PURE ID: | 13681162 |
ISBN: | 9781728131658 |
DOI: | 10.1109/USBEREIT48449.2020.9117689 |
Appears in Collections: | Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC |
Files in This Item:
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2-s2.0-85089654270.pdf | 207,71 kB | Adobe PDF | View/Open |
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