Education Background
2018–2022 Ph.D., Department of Engineering Mechanics, Tsinghua University
2015–2017 M.Eng., School of Energy Science and Engineering, Harbin Institute of Technology
2011–2015 B.Eng., School of Energy Science and Engineering, Harbin Institute of Technology
Work Experiences
2026–present Assistant Professor, Center for Combustion Energy and School of Aerospace Engineering, Tsinghua University
2023–2026 Postdoctoral Research Associate, Department of Chemistry, University of Oxford
Other Professional Positions
Reviewer for journals including npj Computational Materials, The Journal of Chemical Physics, and Machine Learning: Science and Technology
Research Areas and Interests
Micro- and nanoscale thermal transport, machine learning, thermal management of electronic devices, energy storage materials
Honors and Awards
2023 Journal of Physics: Condensed Matter “Emerging Leaders 2023”
2022 Tsinghua University Outstanding Doctoral Dissertation Award
2020 “Wang Buxuan–Guo Zengyuan” Young Scholar Best Paper Award, Chinese Society of Engineering Thermophysics
Books and Patents
Thermophysical Property Calculator for RP-3 Aviation Kerosene (registered software copyright)
Journal Publications
Publications (ORCID: 0000-0002-5948-7031)
Google Scholar: https://scholar.google.com/citations?user=TI270zcAAAAJ
Selected Recent Publications:
1. Y Liu, A Madanchi, A Anker, L Simine, V Deringer. The amorphous state as a frontier in computational materials design. Nature Reviews Materials, 2025, 10: 228–241.
2. Y Liu, H Liang, L Yang, G Yang, H Yang, S Song, Z Mei, G Csányi, B Cao. Unraveling thermal transport correlated with atomistic structures in amorphous gallium oxide via machine learning combined with experiments. Advanced Materials, 2023, 35: 2210873.
3. Y Liu, J Morrow, C Ertural, N Fragapane, J Gardner, A Naik, Y Zhou, J George, V Deringer. An automated framework for exploring and learning potential-energy surfaces. Nature Communications, 2025, 16: 7666.
4. Y Liu, Y Zhou, R Ademuwagun, L Walterbos, J George, S Elliott, V Deringer. Medium-range structural order in amorphous arsenic. Journal of the American Chemical Society, 2026, 148 (9), 9400-9412.
5. L Guo, Y Liu, Z Chen, H Yang, D Donadio, B Cao. Generative deep learning for discovering ultrahigh thermal conductivity materials. npj Computational Materials, 2025, 11: 97.
6. Y Liu, J Yang, G Xin, L Liu, G Csányi, B Cao. Machine learning interatomic potential developed for molecular simulations on thermal properties of β-Ga2O3. The Journal of Chemical Physics, 2020, 153: 144501.
7. Y Liu, W Hong, B Cao. Machine learning for predicting thermodynamic properties of pure fluids and their mixtures. Energy, 2019, 116091.
8. Y Liu, X Liu, B Cao. Graph attention neural networks for mapping materials and molecules beyond short-range interatomic correlations. Journal of Physics: Condensed Matter, 2024, 36: 215901.
9. Y Liu, W Hong, B Cao. MolNet-3D: Deep learning of molecular representations and properties from 3D structural topography. Advanced Theory and Simulations, 2022, 5: 2200037.
10. L Pasca, Y Liu, A Anker, L Steier, V Deringer. Machine-learning-driven modelling of amorphous and polycrystalline BaZrS3. Journal of Materials Chemistry A, 2025, 13(41): 35447-35454.
