Deep variational free energy prediction of dense hydrogen solid at 1200 K
Source abstract
Abstract
We perform deep variational free energy calculations to investigate the dense hydrogen system at 1200 K and high pressures. In this computational framework, neural networks are used to model the free energy through the proton Boltzmann distribution and the electron wavefunction. By directly minimizing the free energy, our results reveal the emergence of a crystalline order associated with the center of mass of hydrogen molecules at approximately 180 GPa. This transition from atomic liquid to a molecular solid is marked by discontinuities in both the pressure and thermal entropy. Additionally, we discuss the broader implications and limitations of these findings in the context of recent studies of dense hydrogen under similar conditions.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Quantum, superfluid, helium dynamics · High-pressure geophysics and materials · Nuclear physics research studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Physics, Chinese Academy of Sciences
AI for Science Institute
University of Zürich
Princeton University
DP Technology
Institute of Applied Physics and Computational Mathematics
Funding
National Natural Science Foundation of China · Chinese Academy of Sciences · National Key Research and Development Program of China
References (52)
Show source-provided references
1. 10.1103/RevModPhys.66.67110.1103/RevModPhys.66.671 ↗
2. 10.1126/science.239.4844.113110.1126/science.239.4844.1131 ↗
3. 10.1038/383702a010.1038/383702a0 ↗
4. 10.1103/PhysRevB.82.06010110.1103/PhysRevB.82.060101 ↗
5. 10.1038/s41586-019-1565-910.1038/s41586-019-1565-9 ↗
6. 10.1103/PhysRevB.101.09410410.1103/PhysRevB.101.094104 ↗
7. 10.1063/5.019808010.1063/5.0198080 ↗
8. 10.1063/1.174959010.1063/1.1749590 ↗
9. L. Landau · Acta Physicochim. USSR · 1943 · 18 · 194
10. 10.1103/PhysRevLett.76.186010.1103/PhysRevLett.76.1860 ↗
11. 10.1103/PhysRevLett.99.18500110.1103/PhysRevLett.99.185001 ↗
12. 10.1126/science.aaa747110.1126/science.aaa7471 ↗
13. 10.1073/pnas.170791811410.1073/pnas.1707918114 ↗
14. 10.1126/science.aat097010.1126/science.aat0970 ↗
15. 10.1103/PhysRevB.82.19510710.1103/PhysRevB.82.195107 ↗
16. 10.1103/PhysRevResearch.2.03206510.1103/PhysRevResearch.2.032065 ↗
17. 10.1103/PhysRevLett.126.22570110.1103/PhysRevLett.126.225701 ↗
18. 10.1103/PhysRevLett.100.11450110.1103/PhysRevLett.100.114501 ↗
19. 10.1073/pnas.100730910710.1073/pnas.1007309107 ↗
20. 10.1073/pnas.160385311310.1073/pnas.1603853113 ↗
21. 10.1103/PhysRevLett.76.124010.1103/PhysRevLett.76.1240 ↗
22. 10.1038/ncomms448710.1038/ncomms4487 ↗
23. 10.1038/s41586-020-2677-y10.1038/s41586-020-2677-y ↗
24. 10.1038/s41586-021-04078-x10.1038/s41586-021-04078-x ↗
25. 10.1103/PhysRevB.106.L04110510.1103/PhysRevB.106.L041105 ↗
26. 10.1103/PhysRevE.111.04530710.1103/PhysRevE.111.045307 ↗
27. 10.1103/PhysRevLett.130.07610210.1103/PhysRevLett.130.076102 ↗
28. 10.1103/PhysRevLett.131.12650110.1103/PhysRevLett.131.126501 ↗
29. 10.1103/PhysRevLett.93.14640210.1103/PhysRevLett.93.146402 ↗
30. 10.1103/PhysRevB.88.01411510.1103/PhysRevB.88.014115 ↗
31. 10.1038/s41524-023-01120-610.1038/s41524-023-01120-6 ↗
32. I. Goodfellow · 2016
33. 10.1103/PhysRevLett.121.26060110.1103/PhysRevLett.121.260601 ↗
34. 10.1103/PhysRevLett.122.08060210.1103/PhysRevLett.122.080602 ↗
35. 10.1103/PhysRevLett.120.14300110.1103/PhysRevLett.120.143001 ↗
36. N. Gao · 2022
37. 10.1038/s43588-022-00228-x10.1038/s43588-022-00228-x ↗
38. 10.1103/PhysRevResearch.2.03342910.1103/PhysRevResearch.2.033429 ↗
39. 10.1038/s41467-022-35627-110.1038/s41467-022-35627-1 ↗
40. 10.1103/PhysRevE.64.01670210.1103/PhysRevE.64.016702 ↗
41. 10.1103/PhysRevLett.120.02570110.1103/PhysRevLett.120.025701 ↗
42. 10.1023/A:100892321502810.1023/A:1008923215028 ↗
43. 10.21468/SciPostPhys.14.6.15410.21468/SciPostPhys.14.6.154 ↗
44. 10.1063/5.020925510.1063/5.0209255 ↗
45. 10.1063/1.160437910.1063/1.1604379 ↗
46. 10.1038/s41586-021-03819-210.1038/s41586-021-03819-2 ↗
47. 10.1016/j.cpc.2020.10720610.1016/j.cpc.2020.107206 ↗
48. 10.1088/0953-8984/22/44/44550110.1088/0953-8984/22/44/445501 ↗
49. 10.1016/j.commatsci.2015.07.00410.1016/j.commatsci.2015.07.004 ↗
50. 10.1103/PhysRevLett.77.386510.1103/PhysRevLett.77.3865 ↗
51. L. Zhang · Adv. Neural Inform. Process. Syst. · 2018 · 31
52. 10.1088/2632-2153/ac6b1610.1088/2632-2153/ac6b16 ↗