/Research-group-machine-learning-potential-dataset

This project primarily aims to open-source basic training sets and related input parameters from our research group's work in the field of machine-learned potential functions.

About Our Group

Our research group focuses on theoretical studies and numerical simulations of nanoscale heat conduction, investigating both normal and anomalous phonon transport. We explore the regulation of thermal energy in micro- and nanostructures, while fostering interdisciplinary collaborations with emerging fields such as materials informatics, materials science, and energy engineering. Additionally, we aim to optimize the design of thermal functional materials and devices by leveraging deep machine learning and artificial intelligence.

About this project

This project aims to open-source the foundational dataset from our team's work in the field of machine learning potential functions. We hope this will provide a reliable resource for researchers and contribute to addressing data science challenges in this area. In the future, we will continue to release additional datasets from our work. Stay tuned for updates.

Our related work

If you use our results, please make sure to cite our work, as this will greatly encourage and support our efforts. Below is the relevant research:

  • Unified deep learning network for enhanced accuracy in predicting thermal conductivity of bilayer graphene, hexagonal boron nitride, and their heterostructures. https://doi.org/10.1063/5.0201698
  • Unraveling the mechanisms of thermal boundary conductance at the graphene-silicon interface: Insights from ballistic, diffusive, and localized phonon transport regimes. https://doi.org/10.1103/PhysRevB.109.115302
  • Modulation of interface modes for resonance-induced enhancement of the interfacial thermal conductance in pillar-based Si/Ge nanowires. https://doi.org/10.1103/PhysRevB.108.235426
  • To be continued......

Other heat transport related research

If you're interested in our research on heat transport, feel free to explore our related work:

Contact information

If you have any questions, please contact us at shiqian@ynu.edu.cn