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Beijing Institute of Technology
- Beijing, China
- https://tmm-group.github.io
- @GangTang7
- https://scholar.google.com/citations?user=qaQa3T4AAAAJ&hl=zh-CN
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VARiable-cell Nudged Elastic Band code with universal first-frinciples calculators
A compact recurrent reinterpretation of CGCNN for materials informatics: SR-CGCNN achieves near-CGCNN-3 accuracy for formation-energy and band-gap prediction while using only 34.5% of the convoluti…
A LOBSTER's postprocessor that can automatically plot the output from Local-Orbital Basis Suite Towards Electronic-Structure Reconstruction (available via http://cohp.de/)
Atomistic foundation models for materials simulation and discovery. Research previews from Kairos Materials.
Public releases of the Clinamen2 optimization framework
Reproducible GeS tutorials and numerical examples for WannierNLQG
AI-ready SISSO analysis and evidence-driven model selection.
Implementation for Packora: Systematic Design for Generative Molecular Crystal Structure Prediction
DSHDesktop:DeepSeek Harness Desktop / DeepSeek Harness 桌面版
WannierNLQG is a Julia package for nonlinear responses and quantum-geometric quantities from Wannier tight-binding models.
[ICML 2026] For equivariant high-order tensor prediction.
All code relating to running the workflow and performing analysis for our spin-polarized hybrid-DFT database of 2,500+ Cs2BB'X6 Halide Double Perovskites.
quacc is a flexible platform for computational materials science and quantum chemistry that is built for the big data era.
Library for computing anisotropy extension to SOAP descriptors
A toolkit that integrates DFT codes with Bayesian optimization to explore stable atomic configurations in materials.
Automatic Reactions KInetic MEchanism DEsign.
An open-source active learning framework for training machine-learned interatomic potentials
WannierBSE-MATLAB is a high-performance computational suite engineered for solving the Bethe-Salpeter Equation (BSE) within the Wannier Tight-Binding (WTB) framework, specialized for 2D materials.
Angular relational knowledge distillation of machine learning interatomic potentials for scalable catalyst exploration