Area of research
Condensed Matter Physics · Materials Chemistry
Research interest
Research interests include Materials science, Condensed matter physics, Physics, Computer science, Superconductivity, and Hubbard model.
Machine Learning and First-Principle Predictions of Materials with Low Lattice Thermal Conductivity
First-principles calculation of Hubbard U for Terbium metal under high pressure
Roadmap on artificial intelligence and big data techniques for superconductivity
Structure prediction and materials design with generative neural networks
Superconducting phases of the square-lattice extended Hubbard model
High Entropy Borides Synthesized by the Thermal Reduction of Metal Oxides in a Microwave Plasma
Machine learning the relationship between Debye temperature and superconducting transition temperature
Properties of high entropy borides synthesized via microwave-induced plasma
High-Entropy Borides under Extreme Environment of Pressures and Temperatures
Machine learning and evolutionary prediction of superhard B-C-N compounds
Fluctuating Nature of Light-Enhanced <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>d</mml:mi></mml:math>-Wave Superconductivity: A Time-Dependent Variational Non-Gaussian Exact Diagonalization Study
LaN structural and topological transitions driven by temperature and pressure
Superhard Boron-Rich Boron Carbide with Controlled Degree of Crystallinity
Experimental and Computational Studies on Superhard Material Rhenium Diboride under Ultrahigh Pressures
Numerical investigation of spin excitations in a doped spin chain
Light-Enhanced Spin Fluctuations and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>d</mml:mi></mml:math>-Wave Superconductivity at a Phase Boundary
Numerically exploring the 1D-2D dimensional crossover on spin dynamics in the doped Hubbard model
Characterizing the three-orbital Hubbard model with determinant quantum Monte Carlo
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