Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session E18: Challenges in Designing Ambient Condition SuperconductorsInvited Live
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Sponsoring Units: DCOMP Chair: James Hamlin, University of Florida |
Tuesday, March 16, 2021 8:00AM - 8:36AM Live |
E18.00001: Ab-initio design of new high-Tc room-pressure conventional superconductors Invited Speaker: Lilia Boeri In the last five years, ab-initio material design has reshaped the landscape of superconductivity research, leading to an impressive acceleration in new material discoveries. [1] |
Tuesday, March 16, 2021 8:36AM - 9:12AM Live |
E18.00002: Physics of Light Dense Matter: Quantum and Classical Effects Invited Speaker: Shanti Deemyad Restricting the volume of a material, through application of pressure, changes the dominance of interactions within the material, and exposes unnatural states of matter not found in our predominantly adiabatic universe. One of the most exotic phenomena in condensed matter is the phase transitions purely driven by quantum effects. While quantum fluctuations in electronic states are always relevant, it is also possible to observe quantum effects in lattice of very light elements. At ambient conditions, the lightest metal of the periodic system is lithium. Similar to hydrogen and helium even at zero temperature lattice of lithium remains far from static and a fascinating system to explore the lattice quantum effects in light dense matter. |
Tuesday, March 16, 2021 9:12AM - 9:48AM Live |
E18.00003: Towards Room Temperature Superconductivity in Hydride-Based Materials Under Pressure Invited Speaker: Eva Zurek The pressure variable opens the door towards the synthesis of materials with unique properties, e.g. superconductivity, hydrogen storage media, high-energy density and superhard materials. Under pressure elements that would not normally combine may form stable compounds or they may adopt novel stoichiometries. As a result, we cannot use our chemical intuition developed at 1 atm to predict phases that become stable when compressed. To facilitate the prediction of the crystal structures of novel materials, without any experimental information, we have developed XtalOpt, an evolutionary algorithm for crystal structure prediction. XtalOpt has been applied to predict the structures of hydrides with unique compositions that become stable at pressures attainable in diamond anvil cells. In the ternary hydride system two different classes of superconductors composed of S and H atoms have been discovered - methane intercalated H3S perovskites with the CSH7 stoichiometry, and phases containing SH honeycomb sheets. We also predict a superconducting RbB3Si3 phase in the bipartite sodalite structure that could be synthesized at mild pressures and quenched to 1 atm. |
Tuesday, March 16, 2021 9:48AM - 10:24AM On Demand |
E18.00004: Machine learning the functional form of the superconducting critical temperature Invited Speaker: Stephen Raymond Xie Predicting the critical temperature Tc of superconductors is a difficult task, even for electron-phonon systems. We build on earlier efforts by McMillan [1] and Allen and Dynes [2] to model Tc from various measures of the phonon spectrum and the electron-phonon interaction by using machine learning algorithms. Specifically, we use symbolic regression implemented in the Sure Independence and Sparsifying Operator (SISSO) method [3] to identify new, physically interpretable equations for Tc as a function of a small number of physical quantities. We show that our first model [4], trained using the relatively small Tc < 10K data tested by Allen and Dynes, improves upon the Allen-Dynes fit and can reasonably generalize to superconducting materials with higher Tc such as H3S. To address the limitations of the Allen-Dynes equation arising from their selection of spectral function α2F(ω) shapes, we generate a new dataset using Eliashberg Theory and more α2F(ω) examples, ranging from bimodal Einstein models to calculated spectra of polyhydrides. Furthermore, we explore the use of variational autoencoders to augment the data. By incorporating physical insights and constraints into a data-driven approach, we demonstrate that machine-learning methods can identify the relevant physical quantities and obtain predictive equations using small but high-quality datasets. |
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