Quantum Monte Carlo generation of machine learning potentials for hydrogen-based high-temperature superconductors

Hydrogen-based compounds have been in the spotlight of the condensed matter community since the discovery of high-temperature superconductivity in sulfur hydride under high pressure. This has been followed by other findings, revealing an entire family of hydrogen-based compounds that share the desirable property of superconducting at very high temperature, breaking all previous records. 

The possibility of increasing the critical temperature by playing with hydrogen chemistry opened the door to a new fertile research ground, in the quest for higher superconducting temperatures and lower (and so more technologically exploitable) pressures. From the theory viewpoint, this quest is challenged by the peculiar properties of hydrogen-based superconductors: strong nuclear quantum effects, large phonon anharmonicity, structural near degeneracy, competing energy scales between electrons and nuclei. 

In this project, this problem will be tackled by combining accurate quantum Monte Carlo (QMC) electronic energies and nuclear forces with an advanced treatment of lattice vibrations, with full inclusion of quantum anharmonicity. This will be achieved by the generation of machine learning potentials, based on QMC-quality training sets, which will then be used in path integral molecular dynamics calculations to simulate lattice dynamics, in targeted compounds, such as pristine hydrogen and the most promising hydrides.