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Searching for ambient-pressure conventional superconductors with critical temperatures (TC) higher than 40 K is a key challenge in the field of high-temperature superconductivity, mainly due to lack of efficient and effective models to…

Understanding ionic transport in halide solid electrolytes is essential for advancing next-generation solid-state batteries. This work demonstrates the effectiveness of fine-tuning the Crystal Hamiltonian Graph Network (CHGNet) universal…

Fluid ferroelectrics, a recently discovered class of liquid crystals that exhibit switchable, long-range polar order, offer opportunities in ultrafast electro-optic technologies, responsive soft matter, and next-generation energy materials.…

Accurately predicting the electronic energy deposition of ions in materials is an important challenge in both fundamental and applied research. While employing ab initio simulations to investigate electronic stopping of ions in matter holds…

Systematic study of electrical resistivity of Hf_{100-x}Fe_x (x=20,25), Hf_{100-x}Cu_x (x=30,40,50), and Ti_{65}Cu_{35} metallic glasses has been done in the temperature range 0.3 K - 290 K, and in magnetic fields B <= 5 T. All Hf-base…

Recommender systems rely on Collaborative Filtering (CF) to predict user preferences by leveraging patterns in historical user-item interactions. While traditional CF methods primarily focus on learning compact vector embeddings for users…

In recent years, metal hydride research has become one of the driving forces of the high-pressure community, as it is believed to hold the key to superconductivity close to ambient temperature. While numerous novel metal hydride compounds…

Considering the prevalence of the power-law distribution in user-item networks, hyperbolic space has attracted considerable attention and achieved impressive performance in the recommender system recently. The advantage of hyperbolic…

Hydrogen-rich materials are the most promising candidates for high-temperature conventional superconductors under ambient pressure. Multinary hydrides have abundant structural configurations and are more promising to find high-temperature…

In this work, we have proposed a data-driven screening framework combining the interpretable machine learning with high-throughput calculations to identify a series of metal oxides that exhibit both high-temperature tolerance and high power…

Knowledge of the behavior of hydrogen in metal hydrides is the key for understanding their electronic properties. So far, no experimental methods exist to access these properties beyond 100 GPa, where high-Tc superconductivity emerges.…

Regression machine learning is widely applied to predict various materials. However, insufficient materials data usually leads to a poor performance. Here, we develop a new voting data-driven method that could generally improve the…

The experimental search for new thermoelectric materials remains largely confined to a limited set of successful chemical and structural families, such as chalcogenides, skutterudites, and Zintl phases. In principle, computational tools…

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…

Descriptors are physically-inspired schemes for representing atomistic systems that play a central role in the construction of models of potential energy surfaces. Although physical intuition can be flexibly encoded into descriptor schemes,…

In this study, we present a framework aimed at enhancing molecular property prediction through the integration of local descriptors obtained from large-scale pretrained machine learning potentials into three-dimensional graph neural…

Publicly available collections of drug-like molecules have grown to comprise 10s of billions of possibilities in recent history due to advances in chemical synthesis. Traditional methods for identifying "hit" molecules from a large…

We predict that superconducting particles will show an apparent increase in thickness at low temperatures when measured by electron holography. This will result not from a real thickness increase, rather from an increase in the mean inner…

Existing molecular machine learning force fields (MLFFs) generally focus on the learning of atoms, molecules, and simple quantum chemical properties (such as energy and force), but ignore the importance of electron density (ED) $\rho(r)$ in…

The use of high pressure to realize superconductivity in the vicinity of room temperature has a long history, much of it focused on achieving this in hydrogen rich materials. This paper provides a brief overview of the work presented at…