Abstract Scope |
Monte Carlo methods are robust computational techniques to study thermodynamics of matter. When combined with high-fidelity first-principles calculations such as density functional theory, it is possible to obtain finite-temperature materials properties like phase transitions and stabilities to a practical accuracy. However, this is computationally inefficient for general applications in practice because a large number of Monte Carlo proposals would be rejected. To fully make use of the information obtained from first-principles calculations, we instead construct the Hamiltonian for a physical system by training accurate neural-network surrogate models from first-principles calculations. The resulting neural-network-based model Hamiltonians then allow us to perform Monte Carlo simulations more efficiently, while retaining high accuracy for the thermodynamics. This work combines the use of high-performance computing and machine learning techniques to accelerate physics simulations, which will be demonstrated through applications to selected spin and alloy systems. |