6533b861fe1ef96bd12c5a05
RESEARCH PRODUCT
Ab initioquality neural-network potential for sodium
Hagai EshetMichele ParrinelloThomas D. KühneJörg BehlerRustam Z. Khaliullinsubject
Physicochemical ProcessesCondensed Matter - Materials ScienceMaterials scienceStatistical Mechanics (cond-mat.stat-mech)Artificial neural networkSodiumAb initioMaterials Science (cond-mat.mtrl-sci)FOS: Physical sciencesThermodynamicschemistry.chemical_elementInteratomic potentialCondensed Matter PhysicsElectronic Optical and Magnetic MaterialsCrystalQuality (physics)chemistryCondensed Matter - Statistical Mechanicsdescription
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, cI16 crystal phases in the P-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-T range enables molecular dynamics simulations of physicochemical processes in HPHT sodium of unprecedented quality.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2010-05-14 | Physical Review B |