材料科学
Artificial intelligence (AI) is rapidly emerging as a new paradigm of scientific discovery, namely data-driven science, across nearly all scientific disciplines. In materials science and engineering, AI has already begun to exert a…
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…
Anti-Heusler alloys, being a new addition to the Heusler alloys family, exhibit atomic disorders, and almost all of them are reported as a re-entrant spin-glass system. Although such spin-glass feature is generally attributed to the…
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…
Collective electronic excitations, including plasmons, excitons, and intra- and interband transitions, play a central role in determining the dynamic screening, optical response, and energy transport properties of materials. Recent advances…
Understanding structure-property relationships in complex materials requires integrating complementary measurements across multiple length scales. Here we propose an interpretable "multimodal" machine learning framework that unifies…
We investigate the structural, mechanical, and thermoelectric properties of lead-free double halide perovskites Cs2TeX6 (X = Cl, Br, I) using first-principles calculations and semiclassical Boltzmann transport theory. The HSE06 band gap is…
Bimetallic catalysts provide new routes toward sustainable ammonia synthesis, but the structural dynamics controlling their performance under real-world conditions remain poorly understood. Here, we combine in situ gas-cell and multimodal…
The geometry and binding energy of excitons, set by electron-hole wavefunction distributions, are fundamental factors that underpin their many-body interactions and determine optoelectronic properties of semiconductors. However, in typical…
This work demonstrates that the convex hull of formation energies for solid compounds involving elements from hydrogen to uranium admits a remarkably simple description over the 92-dimensional space of chemical compositions, despite the…
In the present work, we revisit the problem of the inhomogeneous electron gas under the influence of a weak external potential, which allows us to calculate the gradient corrections to the density functional within linear response, an…
Emissive metal halide perovskites (MHPs) have emerged as excellent candidates for next-generation optoelectronics due to their sharp color purity, inexpensive processing, and bandgap tunability. However, the development of violet and…
In recent years, interest in 2D Janus materials has grown exponentially, particularly with regard to their applications in spintronics and optoelectronic devices. The defining feature of Janus materials is the ordered arrangement of…
Materials discovery is a cornerstone of modern technological advancement, yet it remains constrained by traditional trial-and-error paradigms and the inherent bias of human intuition. Artificial intelligence (AI) has emerged as a…
Materials with interesting physical properties are often designed based on our understanding of the target physical effects. The physical properties can be either explicitly observed ("apparent") or concealed by the perceived symmetry…
We present a first-principles study of the multimode Jahn-Teller (JT) effect in the exctied $^{3}E$ state of the negatively charged nitrogen-vacancy (NV) center in diamond. Using density functional theory combined with an intrinsic…
The second-order optical susceptibility of semiconductors $\chi_{ijk}^{(2)}(-2\omega;\omega,\omega)$ finds application in metrology, spectroscopy, telecommunications, material characterization, and quantum information. Pioneering…
Accurate structural relaxation is critical for advanced materials design. Traditional approaches built on physics-derived first-principles calculations are computationally expensive, motivating the creation of machine-learning interatomic…
Topology, as a mathematical concept, has been introduced into condensed matter physics since the discovery of quantum Hall effect, which characterizes new physical scenario beyond the Landau theory. The topologically protected physical…
The increasing importance of artificial intelligence and machine learning in materials research has created demand for automated, high-throughput characterization techniques capable of rapidly generating large data sets. We describe here a…