Related papers: Refining Tc Prediction in Hydrides via Symbolic-Re…
Intuitively, doping represents one of the most promising avenues for optimization of best prospect superconductors (SC) such as conventional high-pressure SCs with record critical temperatures. However, doping at high pressure (HP) is very…
In this study, we explore lithium-doped stable molecular hydrogen structures by performing first-principles crystal structure searches across varying compositions in the Li-H system under high pressure. Our search reveals a cubic phase of…
Thermoelectric materials can generate clean energy by transforming waste heat into electricity. The effectiveness of thermoelectric materials is measured by the dimensionless figure of merit, ZT. The quest for high ZT materials has drawn…
We propose an efficient computational methodology for predicting the synthesizability of high entropy oxides (HEOs) in a large space of possible candidate compounds. HEOs are a growing field with an enormous potential chemical composition…
Developing high-entropy ceramics (HECs) with ultra-high melting points (Tm) is crucial for their applications in ultra-high-temperature environments. However, related research has seldom been reported. Here, taking high-entropy diborides…
Machine learning has emerged as a powerful tool for predicting molecular properties in chemical reaction networks with reduced computational cost. However, accurately predicting energies of transition state (TS) structures remains a…
We cast the relation between the chemical composition of a solid-state material and its superconducting critical temperature (Tc) as a statistical learning problem with reduced complexity. Training of query-aware similarity-based ridge…
Electrides are exotic compounds in which excess electrons occupy interstitial regions of the crystal lattice and serve as anions, exhibiting exceptional properties such as low work function, high electron mobility, and strong catalytic…
Utilizing the high stability of calcium and rare earth hydrides, CaFeAsF1-xHx (x = 0.0-1.0) and SmFeAsO1-xHx (x = 0.0-0.47) have been first synthesized using high pressure to form hydrogen-substituted 1111 type iron-arsenide…
The rapid development of computational materials science powered by machine learning (ML) is gradually leading to solutions to several previously intractable scientific problems. One of the most prominent is machine learning interatomic…
Metallic hydrogen and hydride materials stand as promising avenues to achieve room-temperature superconductivity. Characterized by their high phonon frequencies and moderate coupling strengths, several high-pressure hydrides were…
We develop a non-perturbative approach for calculating the superconducting transition temperatures ($T_{c}$) of liquids. The electron-electron scattering amplitude induced by electron-phonon coupling (EPC), from which the effective pairing…
We employed a machine-learning assisted approach to search for superconducting hydrides under ambient pressure within an extensive dataset comprising over 150 000 compounds. Our investigation yielded around 50 systems with transition…
Pressurized hydrogen-rich compounds, which could be viewed as precompressed metallic hydrogen, exhibit high superconductivity, thereby providing a viable route toward the discovery of high-temperature superconductors. Of particular interest…
Iron-based superconductors have grown to be a new continent of high Tc superconductors comparable to cuprates. The optimal critical temperature (Tc) of 56 K in electron-doped 1111-type iron oxypnictides attracts considerable attention of…
Data-driven approaches for material discovery and design have been accelerated by emerging efforts in machine learning. However, general representations of crystals to explore the vast material search space remain limited. We introduce a…
Electrides are characterized by electron density highly localized in interstitial sites, which do not coincide with the interatomic contacts. The rigorous quantum mechanical definition of electrides is based upon topological criteria…
Hydrofluoroolefins are considered the most promising next-generation refrigerants due to their extremely low global warming potential values, which can effectively mitigate the global warming effect. However, the lack of reliable…
Advanced computational methods are being actively sought for addressing the challenges associated with discovery and development of new combinatorial material such as formulations. A widely adopted approach involves domain informed…
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…