On the prediction of transport properties of ionic liquid using 1-n-butylmethylpyridinium tetrafluoroborate as an example J Ma, Z Zhang, Y Xiang, F Cao, H Sun Molecular Simulation 43 (18), 1502-1512, 2017 | 22 | 2017 |
Machine learning predicts the x-ray photoelectron spectroscopy of the solid electrolyte interface of lithium metal battery Q Sun, Y Xiang, Y Liu, L Xu, T Leng, Y Ye, A Fortunelli, WA Goddard III, ... The Journal of Physical Chemistry Letters 13 (34), 8047-8054, 2022 | 16 | 2022 |
Reaction mechanisms of the initial oligomerization of aluminophosphate Y Xiang, L Xin, JD Deetz, H Sun The Journal of Physical Chemistry A 120 (18), 2902-2910, 2016 | 13 | 2016 |
Predicting single-substance phase diagrams: A kernel approach on graph representations of molecules Y Xiang, YH Tang, H Liu, G Lin, H Sun The Journal of Physical Chemistry A 125 (20), 4488-4497, 2021 | 9 | 2021 |
Inhomogeneous fluid of penetrable-spheres: Application of the random phase approximation Y Xiang, D Frydel The Journal of Chemical Physics 146 (19), 2017 | 8 | 2017 |
Improving molecular machine learning through adaptive subsampling with active learning Y Wen, Z Li, Y Xiang, D Reker Digital Discovery 2 (4), 1134-1142, 2023 | 7 | 2023 |
A comparative study of marginalized graph kernel and message-passing neural network Y Xiang, YH Tang, G Lin, H Sun Journal of Chemical Information and Modeling 61 (11), 5414-5424, 2021 | 7 | 2021 |
Interpretable Molecular Property Predictions Using Marginalized Graph Kernels Y Xiang, YH Tang, G Lin, D Reker Journal of Chemical Information and Modeling 63 (15), 4633-4640, 2023 | 1 | 2023 |
Yoked learning in molecular data science Z Li, Y Xiang, Y Wen, D Reker Artificial Intelligence in the Life Sciences 5, 100089, 2024 | | 2024 |
Efficient Exploration of Chemical Compound Space Using Active Learning for Prediction of Thermodynamic Properties of Alkane Molecules Y Xiang, YH Tang, Z Gong, H Liu, L Wu, G Lin, H Sun Journal of Chemical Information and Modeling 63 (21), 6515-6524, 2023 | | 2023 |