Random forest

Statistics

Materials science

Support vector machine

Data mining

Computer science

Acoustics

Lattice (music)

Boosting (machine learning)

Physics

Regression

Mathematics

Hyperparameter

Gradient boosting

Artificial intelligence

Artificial neural network

Machine learning

2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)

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.

Machine Learning Methods for Predicting the Lattice Characteristics of Materials