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Superconductivity is a remarkably widespread phenomenon observed in most metals cooled down to very low temperatures. The ubiquity of such conventional superconductors, and the wide range of associated critical temperatures, is readily…

The discovery of superconductivity at 203 K in H3S brought attention back to conventional superconductors whose properties can be described by the Bardeen-Cooper-Schrieffer (BCS) and the Migdal-Eliashberg theories. These theories predict…

Lattice thermal conductivity (TC) of semiconductors is crucial for various applications, ranging from microelectronics to thermoelectrics. Data-driven approach can potentially establish the critical composition-property relationship needed…

Materials Science · Physics 2022-08-30 Zeyu Liu , Meng Jiang , Tengfei Luo

Superconductivity was recently reported in several quasicrystalline systems. These are materials which are structurally ordered, but since they are not translationally invariant, the usual BCS theory does not apply. At the present time, the…

Superconductivity · Physics 2024-03-13 Ying Wang , Gautam Rai , Chris Matsumura , Anuradha Jagannathan , Stephan Haas

The attractive Hubbard model has become a model readily realizable with ultracold atoms on optical lattices. However, the superconducting (superfluid) critical temperatures, $T_c$'s, are still somewhat smaller than the lowest temperatures…

Room temperature superconductivity remains elusive, and hydrogen-base compounds despite remarkable transition temperatures(Tc) typically require extreme pressures that hinder application. To accelerate discovery under moderate pressures, an…

Superconductivity · Physics 2025-11-17 Jiawei Chen , Junhao Peng , Yanwei Liang , Renhai Wang , Huafeng Dong , Wei Zhang

The half-Heusler compound has drawn attention in a variety of fields as a candidate material for thermoelectric energy conversion and spintronics technology. This is because it has various electronic structures, such as semi-metals,…

Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal. The first principles based virtual sampling of this space, for example…

Chemical Physics · Physics 2021-06-23 Bing Huang , O. Anatole von Lilienfeld

Causal discovery from time series is a fundamental task in machine learning. However, its widespread adoption is hindered by a reliance on untestable causal assumptions and by the lack of robustness-oriented evaluation in existing…

Machine Learning · Computer Science 2026-05-01 Huiyang Yi , Xiaojian Shen , Yonggang Wu , Duxin Chen , He Wang , Wenwu Yu

Incorporating Machine Learning (ML) into material property prediction has become a crucial step in accelerating materials discovery. A key challenge is the severe lack of training data, as many properties are too complicated to calculate…

Intrinsic topological superconducting materials are exotic and vital to develop the next-generation topological superconducting devices, topological quantum calculations, and quantum information technologies. Here, we predict the…

High-throughput computational and experimental design of materials aided by machine learning have become an increasingly important field in material science. This area of research has emerged in leaps and bounds in the thermal sciences, in…

Materials Science · Physics 2019-06-17 Hang Zhang , Kedar Hippalgaonkar , Tonio Buonassisi , Ole M. Løvvik , Espen Sagvolden , Ding Ding

Prediction models of lattice thermal conductivity have wide applications in the discovery of thermoelectrics, thermal barrier coatings, and thermal management of semiconductors. kL is notoriously difficult to predict. While classic models…

Materials Science · Physics 2021-09-15 Christian Loftis , Kunpeng Yuan , Yong Zhao , Ming Hu , Jianjun Hu

According to the Anderson theorem, the critical temperature T_c of a disordered superconductor is determined by the average density of states and does not change at the localization threshold. This statement is valid under assumption of a…

Disordered Systems and Neural Networks · Physics 2014-02-12 I. M. Suslov

Designing new, technologically relevant superconductors has long been at the forefront of solid-state physics and chemistry research. However, developing efficient approaches for modeling the thermodynamics of superconducting alloys while…

At this centenary of the discovery of superconductivity, the design of new and more useful superconductors remains as enigmatic as ever. These materials play crucial roles both for fundamental science and applications, and they hold great…

Superconductivity · Physics 2011-10-20 Laura H. Greene , Hamood Z. Arham , Cassandra R. Hunt , Wan Kyu Park

Machine learning (ML) can facilitate efficient thermoelectric (TE) material discovery essential to address the environmental crisis. However, ML models often suffer from poor experimental generalizability despite high metrics. This study…

Materials Science · Physics 2026-02-03 Shoeb Athar , Adrien Mecibah , Philippe Jund

Our understanding of supercooled liquids and glasses has lagged significantly behind that of simple liquids and crystalline solids. This is in part due to the many possibly relevant degrees of freedom that are present due to the disorder…

Machine Learning · Statistics 2018-08-01 Samuel S. Schoenholz

The behaviour of molecules in space is to a large extent governed by where they freeze out or sublimate. The molecular binding energy is thus an important parameter for many astrochemical studies. This parameter is usually determined with…

Astrophysics of Galaxies · Physics 2022-10-05 Torben Villadsen , Niels F. W. Ligterink , Mie Andersen

It has been proposed that the superconducting transition temperature $T_{\mathrm{c}}$ of an unconventional superconductor with a large pairing scale but strong phase fluctuations can be enhanced by coupling it to a metal. However, the…

Superconductivity · Physics 2022-12-05 Philip M. Dee , Steven Johnston , Thomas Maier