83 / 2023-04-13 09:24:56

Combining stochastic density functional theory with deep potential molecular dynamics to study warm dense matter

Warm Dense Matter,Stochastic Density Functional Theory,Born-Oppenheimer Molecular Dynamics,Machine Learning

高压物理与材料科学 > 动高压-海报

Stochastic density functional theory (SDFT) [1,2] and the related mixed stochastic-deterministic density functional theory [3], based on the plane-wave basis set, have been implemented in the first-principles electronic structure software ABACUS [4]. In the traditional finite-temperature Kohn-Sham density functional theory (KSDFT), a well-known orbitals wall limits the first-principles molecular dynamics method to be used at extremely high temperatures. Combining with the Born-Oppenheimer molecular dynamics (BOMD) method, we apply the SDFT method to study systems with temperatures ranging from a few tens of eV to 1000 eV. Additionally, we train machine-learning-based interatomic models [5,6] from the SDFT data and used them in BOMD simulations. As a result, the structural properties, dynamic properties, and transport coefficients of warm dense matter are computed with longer trajectory time and larger system sizes. The abovementioned methods offer a new approach with first-principles accuracy to tackle properties of warm dense matter.  

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