Related papers: Data-driven Design of High Pressure Hydride Superc…
Developing new metal hydrides is a critical step toward efficient hydrogen storage in carbon-neutral energy systems. However, existing materials databases, such as the Materials Project, contain a limited number of well-characterized…
We introduce machine learning (ML) models that predict the electronic structure of materials across a wide temperature range. Our models employ neural networks and are trained on density functional theory (DFT) data. Unlike most other ML…
Following the recent discovery of very high temperature conventional superconductivity in sulfur hydride (critical temperature Tc of 203 K, Ref1) we searched for superconductivity in other hydrides and found that a covalent hydride…
A high-throughput screening using density functional calculations is performed to search for stable boride superconductors from the existing materials database. The workflow employs the fast frozen phonon method as the descriptor to…
Satterthwaite and Toepke (1970 Phys. Rev. Lett. 25 741) predicted high-temperature superconductivity in hydrogen-rich metallic alloys, based on an idea that these compounds should exhibit high Debye frequency of the proton lattice, which…
The discovery of high-temperature conventional superconductivity in H3S with a critical temperature of Tc=203 K was followed by the recent record of Tc ~250 K in the face-centered cubic (fcc) lanthanum hydride LaH10 compound. It was…
The discoveries of high-temperature superconductivity in H3S and LaH10 have excited the search for superconductivity in compressed hydrides. In contrast to rapidly expanding theoretical studies, high-pressure experiments on hydride…
The quest for room-temperature superconductivity has been a long-standing aspiration in the field of materials science, driving extensive research efforts. In this work, we present a novel hydride, YSc$_2$H$_{24}$, which is stable at high…
The recent reports on high-temperature superconductivity above 190 K in hydrogen sulfide at 200 GPa pressure, exceeding all previously discovered superconductors, has greatly invigorated the interest in dense hydrogen-rich solids. In this…
We report the development of a combined machine-learning and high-throughput density functional theory (DFT) framework to accelerate the search for new ferroelectric materials. The framework can predict potential ferroelectric compounds…
The discovery of high-temperature superconductors remains a central challenge in materials science. Hydrogen-rich compounds are among the most promising candidates, as they can exhibit phonon-mediated superconductivity at elevated critical…
Accelerated discovery with machine learning (ML) has begun to provide the advances in efficiency needed to overcome the combinatorial challenge of computational materials design. Nevertheless, ML-accelerated discovery both inherits the…
Room-temperature superconductivity has been one of the most challenging subjects in modern physics. Recent experiments reported that lanthanum hydride LaH$_{10{\pm}x}$ ($x$$<$1) raises a superconducting transition temperature $T_{\rm c}$ up…
The development of machine learning sheds new light on the problem of statistical thermodynamics in multicomponent alloys. However, a data-driven approach to construct the effective Hamiltonian requires sufficiently large data sets, which…
Predicting the critical temperature $T_c$ of new superconductors is a notoriously difficult task, even for electron-phonon paired superconductors for which the theory is relatively well understood. Early attempts by McMillan and Allen and…
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…
The compressed hydrogen-rich compounds have received extensive attention as promising candidates for room temperature superconductivity, however, the high pressure required to stabilize such materials hinders their wide practical…
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…
Materials engineering using atomistic modeling is an essential tool for the development of qubits and quantum sensors. Traditional density-functional theory (DFT) does however not adequately capture the complete physics involved, including…
Density functional theory (DFT) underpins modern atomistic simulations of transition-metal surfaces. It can predict key properties linked to catalytic performance, such as adsorption energies and barrier heights, enabling new paradigms in…