Using Machine Learning to Develop a Quantum-Accurate Inter-Atomic Potential for Large Scale Molecular Dynamics Simulations of Iron under Earth’s Core Conditions

Using Machine Learning to Develop a Quantum-Accurate Inter-Atomic Potential for Large Scale Molecular Dynamics Simulations of Iron under Earth’s Core Conditions

Measurements of iron at extreme pressures do not agree on the melting temperature for conditions comparable with those believed to hold at Earth's core. To attempt to determine the stability of relevant lattices, simulations involving a huge amount of particles are needed. In this thesis, a mac...

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Bibliographic Details
Main Author: Österberg, Viktor
Format: Others
Language:English
Published: KTH, Fysik 2021
Subjects:
Machine learning
Density functional theory
Two–phase simulations
Molecular dynamics
Behler-Parinello Neural network potential
GAP
SNAP
Maskininlärning
Täthetsfunktionalteori
Två–fas simulationer
Molekyldynamik
Behler-Parinello neuronnätspotential
Physical Sciences
Fysik
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298848

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298848

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