- Deep machine learning potentials for multicomponent metallic melts: development, predictability and compositional transferability
2021/10/26 by R. E. Ryltsev, N. M. Chtchelkatchev, Ryltsev, R. E. +1 · 1 citation
Chemistry · Earth and Planetary Sciences · Materials Science · #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 #nanoparticles nucleation surface interactions
- Transport coefficients of multi-component mixtures of noble gases based on ab initio potentials: Viscosity and thermal conductivity
2020/06/30 by Felix Sharipov, Victor J. Benites · 1 citation
Chemical Engineering · Engineering · #Conductivity #Interatomic potential #Krypton #Phase Equilibria and Thermodynamics #Relative density #Relative viscosity #Thermal #Thermal conduction #Thermal conductivity #Thermodynamic and Structural Properties of Metals and Alloys #Thermodynamic properties of mixtures #Viscosity
- One-dimensional model of the quasicrystalline alloy
1987/05/01 by S. E. Burkov · 1 citation
Earth and Planetary Sciences · Materials Science · #Alloy #Condensed matter physics #Density of states #Diffraction #Hard spheres #Interatomic potential #Materials science #Mineralogy and Gemology Studies #Molecular dynamics #Penrose tiling #Physics #Quantum mechanics #Quasicrystal #Quasicrystal Structures and Properties #Quasiperiodic function #Statistical physics #Thermodynamics