mlsanityData to 14 Oct 2026

Revisiting the Broken Symmetry Phase of Solid Hydrogen: A Neural Network Variational Monte Carlo Study

arXiv 2512.17703 ↗First posted 19 Dec 2025cond-mat.str-el cond-mat.mtrl-sci cs.LGcited by 1

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Abstract (arXiv)

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 Cmcm space group symmetry, and we test its stability up to 96 atoms. The predicted structure quantitatively matches the experimental equation of state and gives the closest x-ray diffraction peak-position match among the tested candidates. Furthermore, our group-theoretical analysis provides a symmetry-counting compatibility check between the Cmcm structure and existing Raman and infrared spectroscopic data. Crucially, static density functional theory calculation reveals the Cmcm structure as a dynamically unstable saddle point on the Born-Oppenheimer potential energy surface, demonstrating that a full quantum many-body treatment of the problem is necessary. These results shed new light on the phase diagram of high-pressure hydrogen and call for further experimental verifications.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 121 references that are in the map. First posted on arXiv 19 Dec 2025.

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9 of 121 references.

References · 9

  1. 2020ReZero is All You Need: Fast Convergence at Large Depthcited by 264
  2. 2019Ab-Initio Solution of the Many-Electron Schr"odinger Equation with Deep Neural Networkscited by 200
  3. 2022Discovering Quantum Phase Transitions with Fermionic Neural Networkscited by 26
  4. 2023Accurate Computation of Quantum Excited States with Neural Networkscited by 17
  5. 2025An ab initio foundation model of wavefunctions that accurately describes chemical bond breakingcited by 14
  6. 2023Neural Wave Functions for Superfluidscited by 13
  7. 2023Pairing-based graph neural network for simulating quantum materialscited by 10
  8. 2022Deep Variational Free Energy Approach to Dense Hydrogencited by 7
  9. 2025Spin-Adapted Neural Network Wavefunctions in Real Spacecited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.