R. E. Ryltsev

1. Deep machine learning potentials for multicomponent metallic melts: development, predictability and compositional transferability
   2021/10/26 by R. E. Ryltsev, Ryltsev, R. E., N. M. Chtchelkatchev +1 · 1 citation
   Chemistry · Earth and Planetary Sciences · Materials Science · Physics and Astronomy · #Ab initio #Algorithm #Artificial intelligence #Artificial neural network #Chemical Physics (physics.chem-ph) #Chemistry #Computational Physics (physics.comp-ph) #Computational chemistry #Computer science #FOS: Physical sciences #Hyperparameter #Interatomic potential #Machine Learning in Materials Science #Machine learning #Materials Science (cond-mat.mtrl-sci) #Materials science #Molecular dynamics #Physics #Predictability #Statistical physics #Ternary operation #Transferability #X-ray Diffraction in Crystallography #cond-mat.mtrl-sci #nanoparticles nucleation surface interactions #physics.chem-ph #physics.comp-ph
2. Machine learning interatomic potentials in biomolecular modeling: principles, architectures, and applications
   2025/08/09 by Kobchikova P. P., P.P. Kobchikova, Bakirov B. A. +6 · 1 voice · 1 citation
   Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · #Computational Drug Discovery Methods #Machine Learning in Materials Science #Protein Structure and Dynamics