Speaker: Guglielmo Mazzola (IBM)
Title: Understanding dense hydrogen with neural networks
Abstract:
The phase diagram of hydrogen at Mbar pressures is remarkably complex[1]. In particular, its insulator-to-metal transition, predicted almost one century ago by Wigner and Huntington is crucial in planetary science, and has important technological applications (cfn. the recent discovery of high-temperature superconductivity in dense hydrogen compound [2]).
Experiments at such extreme conditions are challenging and often lead to hard-to-interpret and controversial observations, whereas theoretical investigations are constrained by the huge computational cost of sufficiently accurate quantum mechanical calculations.
In this talk I will first briefly  summarize the current  state-of-the art for the hydrogen-phase diagram, and report a new computational discovery obtained with machine learning.[3]
We provide evidence for a continuous molecular-to-atomic transition in the liquid, with no first-order transition observed above the melting line. This suggests a smooth transition between insulating and metallic layers in giant gas planets, and reconciles existing discrepancies between experiments as a manifestation of supercritical behaviour.

[1] Helled, Mazzola & Redmer. Nature Reviews Physics, volume 2, pages562–574 (2020)
[2] Snider et. al. Nature, volume 586, pages 373–377 (2020)
[3] Cheng, Mazzola, Pickard & Ceriotti, Nature volume 585, pages217–220 (2020)