2010/02/28 by Hagai Eshet, Rustam Z. Khaliullin, Thomas D. Kühne +4 · 2 citations
Chemistry · Earth and Planetary Sciences · Materials Science · Physics and Astronomy · #Ab initio #Ab initio quantum chemistry methods #Artificial neural network #Chemical Thermodynamics and Molecular Structure #Chemical physics #Chemistry #Computational chemistry #Computer science #Crystal (programming language) #High-pressure geophysics and materials #Interatomic potential #Machine Learning in Materials Science #Machine learning #Materials science #Molecular dynamics #Molecule #Organic chemistry #Physics #Potential energy surface #Range (aeronautics) #Representation (politics) #Sodium #Thermodynamics #cond-mat.mtrl-sci #cond-mat.stat-mech
paper · pdf · doi:10.1103/physrevb.81.184107
8 pages, 11 figures, 2 tables
arxiv created 2010/04/19 · openalex publication_date 2010/05/14 · arxiv updated 2015/05/18 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
An interatomic potential for high-pressure high-temperature (HPHT) crystalline and liquid phases of sodium is created using a neural-network (NN) representation of the ab initio potential-energy surface. It is demonstrated that the NN potential provides an ab initio quality description of multiple properties of liquid sodium and bcc, fcc, and cI16 crystal phases in the P\text\ensuremath-T region up to 120 GPa and 1200 K. The unique combination of computational efficiency of the NN potential and its ability to reproduce quantitatively experimental properties of sodium in the wide P\text\ensuremath-T range enables molecular-dynamics simulations of physicochemical processes in HPHT sodium of unprecedented quality.