| Predicting lattice thermal conductivity via machine learning: A mini review Y Luo, M Li, H Yuan, H Liu, Y Fang npj Computational Materials 9 (1), 4, 2023 | 33 | 2023 |
| Predicting the lattice thermal conductivity of alloyed compounds from the perspective of configurational entropy M Li, G Cao, Y Luo, C Sheng, H Liu npj Computational Materials 8 (1), 75, 2022 | 12 | 2022 |
| Machine learning for accelerated prediction of the Seebeck coefficient at arbitrary carrier concentration HM Yuan, SH Han, R Hu, WY Jiao, MK Li, HJ Liu, Y Fang Materials Today Physics 25, 100706, 2022 | 10 | 2022 |
| Surprisingly good thermoelectric performance of monolayer C3N WY Jiao, R Hu, SH Han, YF Luo, HM Yuan, MK Li, HJ Liu Nanotechnology 33 (4), 045401, 2021 | 9 | 2021 |
| HfSe2/GaSe Heterostructure as a Promising Near-Room-Temperature Thermoelectric Material W Jiao, R Hu, H Yuan, S Han, M Li, Q Tang, W Lei, H Liu The Journal of Physical Chemistry C 126 (48), 20326-20331, 2022 | 4 | 2022 |
| Accurate prediction on the lattice thermal conductivities of monolayer systems by a high-throughput descriptor Y Luo, M Li, H Yuan, H Cao, H Liu Journal of Physics D: Applied Physics 56 (4), 045304, 2022 | 3 | 2022 |
| Effects of van der Waals interactions on the phonon transport properties of tetradymite compounds MK Li, CY Sheng, R Hu, SH Han, HM Yuan, HJ Liu New Journal of Physics 23 (8), 083002, 2021 | 2 | 2021 |
| Machine learning of the Γ-point gap and flat bands of twisted bilayer graphene at arbitrary angles X Ma, Y Luo, M Li, W Jiao, H Yuan, H Liu, Y Fang Chinese Physics B 32 (5), 057306, 2023 | 1 | 2023 |
| Tuning the lattice thermal conductivity of Janus SnSSe by interlayer twisting: a machine-learning-based study Y Luo, H Cao, M Li, H Yuan, H Liu New Journal of Physics 26 (4), 043013, 2024 | | 2024 |
| Accelerated Discovery of Advanced Thermoelectric Materials via Transfer Learning M Li, H Yuan, Y Luo, X Ma, H Liu, W Jiang, Y Fang Advanced Energy Materials 13 (23), 2300049, 2023 | | 2023 |
