Self-learning hybrid Monte Carlo: A first-principles approach

First principles quantum Monte Carlo

Present quantumMonte Carlo codes use statistical techniques adapted to find the amplitude of a quantum system or the associated eigenvalues. Thus, they do not use a true physical random source. It is demonstrated that, in fact, quantum probability admits a description based on a specific class of random process at least for the single particle case. Then a first principle Monte Carlo code that ...

Self-learning Monte Carlo method

First Principles Methods: A Perspective from Quantum Monte Carlo

Quantum Monte Carlo methods are among the most accurate algorithms for predicting properties of general quantum systems. We briefly introduce ground state, path integral at finite temperature and coupled electron-ion Monte Carlo methods, their merits and limitations. We then discuss recent calculations using these methods for dense liquid hydrogen as it undergoes a molecular/atomic (metal/insul...

Ordered Phases in Cu2NiZn: A First-Principles Monte Carlo Study

Adaptive kinetic Monte Carlo for first-principles accelerated dynamics.

The adaptive kinetic Monte Carlo method uses minimum-mode following saddle point searches and harmonic transition state theory to model rare-event, state-to-state dynamics in chemical and material systems. The dynamical events can be complex, involve many atoms, and are not constrained to a grid-relaxing many of the limitations of regular kinetic Monte Carlo. By focusing on low energy processes...