Revisiting the Broken Symmetry Phase of Solid Hydrogen: A Neural Network Variational Monte Carlo Study
Shengdu Chai, Chen Lin, Xinyang Dong, Yuqiang Li, Wanli Ouyang, Lei Wang, X.C. Xie

TL;DR
This study employs a neural network variational Monte Carlo approach to identify a new candidate structure for the broken symmetry phase of solid hydrogen at 130 GPa, aligning with experimental data and emphasizing the importance of quantum many-body effects.
Contribution
It introduces a first-principles quantum Monte Carlo framework with neural networks to accurately model electron-nuclear coupling in high-pressure hydrogen phases.
Findings
Identifies a new $Cmcm$ structure candidate for the broken symmetry phase.
Matches experimental equation of state and diffraction data.
Shows static DFT finds the structure dynamically unstable, highlighting the need for quantum many-body treatment.
Abstract
The crystal structure of high-pressure solid hydrogen remains a fundamental open problem. Although the research frontier has mostly shifted toward ultra-high pressure phases above 400 GPa, we show that even the broken symmetry phase observed around 130~GPa requires revisiting due to its intricate coupling of electronic and nuclear degrees of freedom. Here, we develop a first principle quantum Monte Carlo framework based on a deep neural network wave function that treats both electrons and nuclei quantum mechanically within the constant pressure ensemble. Our calculations reveal an unreported ground-state structure candidate for the broken symmetry phase with space group symmetry, and we test its stability up to 96 atoms. The predicted structure quantitatively matches the experimental equation of state and X-ray diffraction patterns. Furthermore, our group-theoretical analysis…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsHigh-pressure geophysics and materials · Quantum, superfluid, helium dynamics · Machine Learning in Materials Science
