$\mathcal{P}^2$: Combining pressure and electrochemistry to synthesize superhydrides

Guan, Pin-Wen; Hemley, Russell J.; Viswanathan, Venkatasubramanian

doi:10.48550/arxiv.2007.15613

Cited by 3 publications

(3 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(3) Could materials be synthesized and stabilized using multiple extreme environments, including magnetic or electric fields? Recent calculations indicate that electrochemical loading could be used to synthesize the materials at modest pressures [48].…”

Section: Current and Future Challengesmentioning

confidence: 99%

The 2021 room-temperature superconductivity roadmap

Boeri

Hennig

Hirschfeld

et al. 2022

J. Phys.: Condens. Matter

137

View full text Add to dashboard Cite

Designing materials with advanced functionalities is the main focus of contemporary solid-state physics and chemistry. Research efforts worldwide are funneled into a few high-end goals, one of the oldest, and most fascinating of which is the search for an ambient temperature superconductor (A-SC). The reason is clear: superconductivity at ambient conditions implies being able to handle, measure and access a single, coherent, macroscopic quantum mechanical state without the limitations associated with cryogenics and pressurization. This would not only open exciting avenues for fundamental research, but also pave the road for a wide range of technological applications, affecting strategic areas such as energy conservation and climate change. In this roadmap we have collected contributions from many of the main actors working on superconductivity, and asked them to share their personal viewpoint on the field. The hope is that this article will serve not only as an instantaneous picture of the status of research, but also as a true roadmap defining the main long-term theoretical and experimental challenges that lie ahead. Interestingly, although the current research in superconductor design is dominated by conventional (phonon-mediated) superconductors, there seems to be a widespread consensus that achieving A-SC may require different pairing mechanisms. In memoriam, to Neil Ashcroft, who inspired us all.

show abstract

Section: Current and Future Challengesmentioning

confidence: 99%

The 2021 room-temperature superconductivity roadmap

Boeri

Hennig

Hirschfeld

et al. 2022

J. Phys.: Condens. Matter

137

View full text Add to dashboard Cite

show abstract

“…The properties of ionic and mixed metal hydrides will probably allow their use as ionic conductors and electrolytes for electrochemical synthesis of hydrides at high pressures, as suggested in ref. [50]. Indeed, calculations show that the hydrogen diffusion rate (~6 × 10 -6 cm 2 s -1 [51]) at high pressures in hydrides -in Li2MgH16 in particular -may be much higher than in known ionic conductors.…”

Section: Classes Of Polyhydridesmentioning

confidence: 99%

High-temperature superconductivity in hydrides

et al. 2021

View full text Add to dashboard Cite

Over the past six years (2015)(2016)(2017)(2018)(2019)(2020)(2021), many superconducting hydrides with critical temperatures TC up to 250 K, which are currently record highs, have been discovered. Now we can already say that a special field of superconductivity has developed. This is hydride superconductivity at ultrahigh pressures. For the most part, the properties of superhydrides are well described by the Migdal-Eliashberg theory of strong electronphonon interaction, especially when anharmonicity of phonons is taken into account. We investigate the isotope effect, the effect of the magnetic field (up to 60-70 T) on the critical temperature and critical current in the hydride samples, and the dependence of Tc on the pressure and the degree of doping. The divergences between the theory and experiment are of interest, especially in the regions of phase stability and in the behavior of the upper critical magnetic fields at low temperatures. We present a retrospective analysis of data of 2015-2021 and describe promising directions for future research of hydride superconductivity.

show abstract

“…To quantify the uncertainty associated with such choice, the ensemble approach was employed here, which has been applied successfully in other fields of computational materials science, e.g., the Bayesian Error Estimation Functional (BEEF) where an ensemble of density functionals are used to estimate the exchange-correlation errors 24 . Especially, the ensemble approach has been applied in thermochemical properties 25 and phase diagrams 26,27 . In the present work, the training dataset was sampled randomly for 100 times forming 100 subsets, and each subset contains 75% of the total training data.…”

Section: Uncertainty Quantificationmentioning

confidence: 99%

MeltNet: Predicting alloy melting temperature by machine learning

Guan¹,

Viswanathan²

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Thermodynamics is fundamental for understanding and synthesizing multi-component materials, while efficient and accurate prediction of it still remain urgent and challenging. As a demonstration of the "Divide and conquer" strategy decomposing a phase diagram into different learnable features, quantitative prediction of melting temperature of binary alloys is made by constructing the machine learning (ML) model "MeltNet" in the present work. The influences of model hyperparameters on the prediction accuracy is systematically studied, and the optimal hyperparameters are obtained by Bayesian optimization. A comprehensive error analysis is made on various aspects including training duration, chemistry and input features. It is found that except a few discrepancies mainly caused by less satisfactory treatment of metalloid/semimetal elements and large melting point difference with poor liquid mixing ability between constituent elements, MeltNet achieves overall success in prediction, especially capturing subtle composition-dependent features in the unseen chemical systems for the first time. The reliability, robustness and accuracy of MeltNet is further largely boosted by introducing the ensemble method with uncertainty quantification. Based on the state-of-the-art underlying techniques, MeltNet achieves a prediction mean average error (MAE) as low as about 120 K, at a minimal computational cost. We believe the present work has a general value for significant acceleration of predicting thermodynamics of complicated multi-component systems.

show abstract

$\mathcal{P}^2$: Combining pressure and electrochemistry to synthesize superhydrides

Cited by 3 publications

References 30 publications

The 2021 room-temperature superconductivity roadmap

The 2021 room-temperature superconductivity roadmap

High-temperature superconductivity in hydrides

MeltNet: Predicting alloy melting temperature by machine learning

Contact Info

Product

Resources

About