Research interests: I am interested in solving the global sustainability challenges by combining machine learning and large scale molecular simulation. I believe it requires integrated innovations in three aspects: 1) designing physics motivated machine learning algorithms; 2) creating infrastructure for large scale molecular simulation; and 3) inventing new collaborative frameworks between computational and experimental experts. I work with a highly interdiscripnary team to rethink the paradigm for materials discovery, aiming for solving sustainability related problems with real-world impact.
Previously: I was a postdoc in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT from 2020 to 2022, co-advised by Tommi Jaakkola and Regina Barzilay. I got my PhD in Materials Science and Engineering at MIT in 2020, advised by Jeffrey C. Grossman. I got my BS in Chemistry at Peking University in 2015. I was a research intern at DeepMind, working with Pushmeet Kohli and James Kirkpatrick in 2019/05. I was a research intern at Google X in 2019/02.
recent news [earlier]
|2022/02||I joined Microsoft Research as a Senior Researcher. I will work on ML for materials in the newly founded molecular modeling intiative.|
|2021/12||I will give a contributed talk (top 2 %) about our latest CDVAE paper at the NeurIPS ML4Physics workshop on Dec. 13, 2021.|
|2021/10||Excited to share our latest preprint “Crystal Diffusion Variational Autoencoder for Periodic Material Generation” on arXiv. Update (2021/12): code and data are now available on our github repo.|
selected publications [full list]
- Crystal Diffusion Variational Autoencoder for Periodic Material GenerationInternational Conference on Learning Representations (ICLR) 2021Contributed talk (top 2 %) at the NeurIPS 2021 ML4PS Workshop
- Charting Lattice Thermal Conductivity for Inorganic Crystals and Discovering Rare Earth Chalcogenides for ThermoelectricsEnergy & Environmental Science 2021
- Accelerating the screening of amorphous polymer electrolytes by learning to reduce random and systematic errors in molecular dynamics simulationsarXiv preprint arXiv:2101.05339 2021Spotlight Talk at NeurIPS 2020 ML4Molecules workshop
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materialsNature communications 2019
- Machine learning enabled computational screening of inorganic solid electrolytes for suppression of dendrite formation in lithium metal anodesACS central science 2018
- Crystal graph convolutional neural networks for an accurate and interpretable prediction of material propertiesPhysical review letters 2018