Literature DB >> 32908269

Evidence for supercritical behaviour of high-pressure liquid hydrogen.

Bingqing Cheng1,2,3, Guglielmo Mazzola4, Chris J Pickard5,6, Michele Ceriotti7,8.   

Abstract

Hydrogen, the simplest and most abundant element in the Universe, develops a remarkably complex behaviour upon compression1. Since Wigner predicted the dissociation and metallization of solid hydrogen at megabar pressures almost a century ago2, several efforts have been made to explain the many unusual properties of dense hydrogen, including a rich and poorly understood solid polymorphism1,3-5, an anomalous melting line6 and the possible transition to a superconducting state7. 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. Here we present a theoretical study of the phase diagram of dense hydrogen that uses machine learning to 'learn' potential-energy surfaces and interatomic forces from reference calculations and then predict them at low computational cost, overcoming length- and timescale limitations. We reproduce both the re-entrant melting behaviour and the polymorphism of the solid phase. Simulations using our machine-learning-based potentials 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.

Entities:  

Year:  2020        PMID: 32908269     DOI: 10.1038/s41586-020-2677-y

Source DB:  PubMed          Journal:  Nature        ISSN: 0028-0836            Impact factor:   49.962


  11 in total

1.  Gaussian Process Regression for Materials and Molecules.

Authors:  Volker L Deringer; Albert P Bartók; Noam Bernstein; David M Wilkins; Michele Ceriotti; Gábor Csányi
Journal:  Chem Rev       Date:  2021-08-16       Impact factor: 60.622

2.  Informing geometric deep learning with electronic interactions to accelerate quantum chemistry.

Authors:  Zhuoran Qiao; Anders S Christensen; Matthew Welborn; Frederick R Manby; Anima Anandkumar; Thomas F Miller
Journal:  Proc Natl Acad Sci U S A       Date:  2022-07-28       Impact factor: 12.779

3.  Nuclear quantum effects on the dynamics and glass behavior of a monatomic liquid with two liquid states.

Authors:  Ali Eltareb; Gustavo E Lopez; Nicolas Giovambattista
Journal:  J Chem Phys       Date:  2022-05-28       Impact factor: 4.304

4.  BIGDML-Towards accurate quantum machine learning force fields for materials.

Authors:  Huziel E Sauceda; Luis E Gálvez-González; Stefan Chmiela; Lauro Oliver Paz-Borbón; Klaus-Robert Müller; Alexandre Tkatchenko
Journal:  Nat Commun       Date:  2022-06-29       Impact factor: 17.694

Review 5.  Ab Initio Machine Learning in Chemical Compound Space.

Authors:  Bing Huang; O Anatole von Lilienfeld
Journal:  Chem Rev       Date:  2021-08-13       Impact factor: 60.622

6.  Unsupervised Learning Methods for Molecular Simulation Data.

Authors:  Aldo Glielmo; Brooke E Husic; Alex Rodriguez; Cecilia Clementi; Frank Noé; Alessandro Laio
Journal:  Chem Rev       Date:  2021-05-04       Impact factor: 60.622

7.  Quantum-mechanical exploration of the phase diagram of water.

Authors:  Aleks Reinhardt; Bingqing Cheng
Journal:  Nat Commun       Date:  2021-01-26       Impact factor: 14.919

8.  Super-strong magnetic field-dominated ion beam dynamics in focusing plasma devices.

Authors:  A Morace; Y Abe; J J Honrubia; N Iwata; Y Arikawa; Y Nakata; T Johzaki; A Yogo; Y Sentoku; K Mima; T Ma; D Mariscal; H Sakagami; T Norimatsu; K Tsubakimoto; J Kawanaka; S Tokita; N Miyanaga; H Shiraga; Y Sakawa; M Nakai; H Azechi; S Fujioka; R Kodama
Journal:  Sci Rep       Date:  2022-04-27       Impact factor: 4.996

9.  Thermodynamics of high-pressure ice phases explored with atomistic simulations.

Authors:  Aleks Reinhardt; Mandy Bethkenhagen; Federica Coppari; Marius Millot; Sebastien Hamel; Bingqing Cheng
Journal:  Nat Commun       Date:  2022-08-10       Impact factor: 17.694

10.  Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.

Authors:  John A Keith; Valentin Vassilev-Galindo; Bingqing Cheng; Stefan Chmiela; Michael Gastegger; Klaus-Robert Müller; Alexandre Tkatchenko
Journal:  Chem Rev       Date:  2021-07-07       Impact factor: 60.622

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