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The discovery of novel superconducting materials is a longstanding challenge in materials science, with a wealth of potential for applications in energy, transportation, and computing. Recent advances in artificial intelligence (AI) have…
Compressed hydrides are promising candidates for high-temperature superconductivity, yet achieving simultaneous structural stability and high-Tc at low pressures remains challenging. Here, we introduce a new mechanism for accomplishing this…
We report that the incorporation of hydroxide ions (OH)- significantly enhances the superconducting transition temperature (Tc) in the LnFeAsO-based superconductors (Ln1111: Ln = La, Ce and Pr). For La1111, Tc of the (OH)- incorporated…
Materials synthesis optimization is constrained by serial feedback processes that rely on manual tools and intuition across multiple siloed modes of characterization. We automate and generalize feature extraction of reflection high-energy…
Thermoelectrics (TEs) are promising candidates for energy harvesting with performance quantified by figure of merit, $ZT$. To accelerate the discovery of high-$ZT$ materials, efforts have focused on identifying compounds with low thermal…
High-temperature superconductivity occurs in strongly correlated materials such as copper oxides and iron-based superconductors. Numerous experimental and theoretical works have been done to identify the key parameters that induce…
Electronic coupling matrix elements are important to the theoretical description of electron transfer processes. However, they are notoriously difficult to obtain accurately from time- dependent density functional theory (TDDFT). Here, we…
Machine learning can accelerate materials discovery. Models perform impressively on many benchmarks. However, strong benchmark performance does not imply that a model learned chemistry. I test a concrete alternative hypothesis: that…
Two hydrogen-rich materials, H$_3$S and LaH$_{10}$, synthesized at megabar pressures, have revolutionized the field of condensed matter physics providing the first glimpse to the solution of the hundred-year-old problem of room temperature…
Inspired by nature, this study employs the Materials Genome Initiative to identify key components of HTSC superconductors. Integrating AI with high-throughput screening, we uncover crucial superconducting "genes". Through HTS techniques and…
In semi-supervised learning, methods that rely on confidence learning to generate pseudo-labels have been widely proposed. However, increasing research finds that when faced with noisy and biased data, the model's representation network is…
Due to its low atomic mass hydrogen is the most promising element to search for high-temperature phononic superconductors. However, metallic phases of hydrogen are only expected at extreme pressures (400 GPa or higher). The measurement of a…
The practically unlimited high-dimensional composition space of high-entropy materials (HEMs) has emerged as an exciting platform for functional materials design and discovery. However, the identification of stable and synthesizable HEMs…
Reactive chemistry of molecular hydrogen at surfaces, notably dissociative sticking and hydrogen evolution, plays a crucial role in energy storage and fuel cells. Theoretical studies can help to decipher underlying mechanisms and reaction…
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…
Superconductivity is a remarkable phenomenon in condensed matter physics, which comprises a fascinating array of properties expected to revolutionize energy-related technologies and pertinent fundamental research. However, the field faces…
First-principles computations are the driving force behind numerous discoveries of hydride-based superconductors, mostly at high pressures, during the last decade. Machine-learning (ML) approaches can further accelerate the future…
In this article we present a generalization of the electron localization function (ELF) that can be used to analyze time-dependent processes. The time-dependent ELF allows the time-resolved observation of the formation, the modulation, and…
The machine learning based approaches efficiently solve the goal of searching the best materials candidate for the targeted properties. The search for topological materials using traditional first-principles and symmetry-based methods often…
As the simplest element in nature, unraveling the phase diagram of hydrogen is a primary task for condensed matter physics. As conjectured many decades ago, in the low-temperature and high-pressure part of the phase diagram, solid hydrogen…