Prediction of bonding energy by structural descriptors of metal nanoalloys
V.S. Myasnichenko1, P.V. Matrenin2, N.Yu. Sdobnyakov1
1 Tver State University
2 Novosibirsk State Technical University
DOI: 10.26456/pcascnn/2021.13.495
Original article
Abstract: The problem of predicting the binding energy for ternary metal nanoparticles and the construction of learning models based on structural descriptors are discussed. Regression dependences of the specific interatomic bond energy were constructed for the ternary Au–Ag–Cu nanosystem. A number of five radial features were used, depending on the pairwise interatomic distance of the nanoparticle structure descriptors. For a more correct assessment of the accuracy, cross-validation was applied, then the results obtained on the validation parts of the sample were averaged. The resulting model limitedly predicts the value of the specific interatomic binding energy within a group of data for nanoparticles of the same composition. For the entire sample the average error in modulus is 14 %. In this case, the model almost accurately determines the composition of a nanoparticle of several variants. The largest value of the coefficient of determination in the entire sample was obtained using an ensemble random forest algorithm. A negative correlation was found between the binding energy of the nanoalloy and the position of the first peak of the radial distribution function for copper atoms.
Keywords: machine learning, radial distribution function, structural descriptor, bond energy, ternary alloy.
- Vladimir S. Myasnichenko – Researcher, General Physics Department, Tver State University
- Pavel V. Matrenin – Ph. D., Senior Lecturer, «Power Supply Systems of Enterprises» Department, Novosibirsk State Technical University
- Nickolay Yu. Sdobnyakov – Ph. D., Docent, General Physics Department, Tver State University
Reference:
Myasnichenko, V.S. Prediction of bonding energy by structural descriptors of metal nanoalloys / V.S. Myasnichenko, P.V. Matrenin, N.Yu. Sdobnyakov // Physical and chemical aspects of the study of clusters, nanostructures and nanomaterials. — 2021. — I. 13. — P. 495-502. DOI: 10.26456/pcascnn/2021.13.495. (In Russian).
Full article (in Russian): download PDF file
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