Interatomic Potential Model Development: Finite‐Temperature Dynamics Machine Learning

Machine Learning methods for interatomic potentials: application to boron carbide

Total energies of crystal structures can be calculated to high precision using quantumbased density functional theory (DFT) methods, but the calculations can be time consuming and scale badly with system size. Boron carbide exhibits disorder in the distribution of boron and carbon atoms among the crystallographic sites. A cluster expansion of the DFT energy in a series of pairs, triplets, etc. ...

Accurate Interatomic Force Fields via Machine Learning with Covariant Kernels

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Development of a bond-valence based interatomic potential for BiFeO3 for accurate molecular dynamics simulations.

We present an atomistic potential for BiFeO(3) based on the principles of bond-valence (BV) and bond-valence vector (BVV) conservation. The validity of this model potential is tested for both canonical ensemble (NVT) and isobaric-isothermal ensemble (NPT) molecular dynamics (MD) simulations. The model reproduces the ferroelectric-to-paraelectric phase transition in both NVT and NPT MD simulati...

Machine learning based Visual Evoked Potential (VEP) Signals Recognition

Introduction: Visual evoked potentials contain certain diagnostic information which have proved to be of importance in the visual systems functional integrity. Due to substantial decrease of amplitude in extra macular stimulation in commonly used pattern VEPs, differentiating normal and abnormal signals can prove to be quite an obstacle. Due to developments of use of machine l...

Title An interatomic potential model for molecular dynamics simulation of silicon etching by Br(+)-containing plasmas