A5060872867- X-ray Diffraction in Crystallography
- Machine Learning in Materials Science
- Ga2O3 and related materials
- Quantum Dots Synthesis And Properties
- Perovskite Materials and Applications
- Chalcogenide Semiconductor Thin Films
- ZnO doping and properties
- Computational Drug Discovery Methods
- Crystallization and Solubility Studies
- GaN-based semiconductor devices and materials
- Fuel Cells and Related Materials
- Photocathodes and Microchannel Plates
- Advanced Thermoelectric Materials and Devices
- Microwave Dielectric Ceramics Synthesis
- Advanced Condensed Matter Physics
- Boron and Carbon Nanomaterials Research
- Solid-state spectroscopy and crystallography
- Thermal Expansion and Ionic Conductivity
University of Science and Technology of China
2023-2025
Beijing Computational Science Research Center
2024-2025
University of California, Santa Barbara
2025
Cu chalcopyrites exhibit excellent thermoelectric performance because of their high thermopower and low thermal conductivity. Conversely, despite that Ag have even lower conductivity than the Cu-based ones, they generally display performance. The underlying physics for disparity between Cu- Ag-based materials remains unclear. In this work we investigate transport ternary $AM{\mathrm{Se}}_{2}$ $(A=\mathrm{Cu}/\mathrm{Ag}; M=\mathrm{Ga}/\mathrm{In})$ using first-principles methods. We reveal...
Abstract Structural search and feature extraction are a central subject in modern materials design, the efficiency of which is currently limited, but can be potentially boosted by machine learning (ML). Here, we develop an ML-based prediction-analysis framework, includes symmetry-based combinatorial crystal optimization program (SCCOP) additive attribution model, to significantly reduce computational costs extract property-related structural features. Our method highly accurate predictive,...
The crystal structure of a material is essentially determined by the nature its chemical bonding. Consequently, atomic coordination intimately correlates with degree ionicity or covalency material. Based on this principle, materials similar compositions can be successfully categorized into different groups. However, counterexamples have recently emerged in complex ternary compounds. For instance, covalent IB-IIIA-VIA2 compounds, such as AgInS2, prefer tetrahedrally coordinated (TCS), while...
Polymorphs commonly exist for various materials, enabling phase engineering diverse material properties. While the crystal structures of different polymorphs can, in principle, be experimentally characterized, interpreting and understanding complex can very challenging. Using Ga_{2}O_{3} as a prototype, here we show that structure γ-Ga_{2}O_{3} has long been misinterpreted from either theory or experiment. By lattice mapping based crystallographic analysis combinatorial search, reveal nature...
Recent angle-resolved photoelectron spectroscopy (ARPES) measurements of the hole effective mass in CsPbBr$_3$ revealed an enhancement $\sim$50 % compared to bare computed from first principles for at $T = 0 K$. This large was interpreted as evidence polaron formation. Employing accurate finite-temperature first-principles calculations, we show that calculated 300 K$ can explain experimental results without invoking polarons. Thermal fluctuations are particularly strong halide perovskites...
Machine learning has boosted the remarkable development of crystal structure prediction (CSP), greatly accelerating modern materials design. However, slow location low-energy regions on potential energy surface (PES) is still a key bottleneck for overall search efficiency. Here, we develop region explorer (LoreX) to rapidly locate PES. This achievement stems from graph-deep-learning-based PES slicing, which classifies structures into different prototypes divide and conquer The accuracy...
Deep-ultraviolet (DUV) light sources are technologically highly important, but DUV light-emitting materials extremely rare; AlN and its alloys the only known so far, significantly limiting chemical structural spaces for design. Here, we perform a high-throughput computational search emitters based on set of carefully designed screening criteria relating to sophisticated electronic structure. In this way, successfully identify 5 promising material candidates that exhibit comparable or higher...
Structural prediction for the discovery of novel materials is a long sought-after goal computational physics and sciences. The success rather limited methods such as simulated annealing method (SA) that require expensive density functional theory (DFT) calculations follow unintelligent search paths. Here machine-learning based crystal combinatorial optimization program (CCOP) with fitting-search style proposed to drastically improve efficiency structural in SA. CCOP uses graph neural network...
Abstract Machine learning has boosted the remarkable development of crystal structure prediction (CSP), greatly accelerating modern materials design.The state-of-the-art methods, however, rely on a giant training dataset, and there is trend to keep increasing as triggered by great success artificial intelligence models.Here, we show that dataset not really necessary.Only around 100 carefully selected samples for target composition already sufficient highly accurate CSP, which demonstrated in...
Abstract Structural search and feature extraction are a central subject in modern materials design, the efficiency of which is currently limited, but can be potentially boosted by machine learning (ML). Here, we develop an ML-based prediction-analysis framework, includes symmetry-based combinatorial crystal optimization program (SCCOP) additive attribution model, to significantly reduce computational costs extract property-related structural features. Our method highly accurate predictive,...