Research interests: I am broadly interested designing physics motivated machine learning algorithms for solving real-world problems related to material design, drug design, and other engineering domains. I study how domain-specific inductive biases can be incorporated into neural networks to improve generalizability and interpretability. I work closely with chemists, material scientists, and other domain experts to deploy machine learning algorithms to accelerate existing workflows and gain scientific insights.
Previously: 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.
|2021/05||Our lastest work “Charting lattice thermal conductivity for inorganic crystals and discovering rare earth chalcogenides for thermoelectrics is published on Energy & Environmental Science.|
|2020/12||I will present our recent work “Accelerate the screening of complex materials by learning to reduce random and systematic errors” as a Spotlight Talk at the NeurIPS ML4Molecules workshop.|
|2020/10||I joined MIT CSAIL as a postdoc. I will be working with Regina Barzilay and Tommi Jaakkola on developing physics motivated ML models for drug and material discovery.|
selected publications [full list]
- Accelerating the screening of amorphous polymer electrolytes by learning to reduce random and systematic errors in molecular dynamics simulationsNeurIPS ML4Molecules workshop 2020Spotlight Talk, full paper at arXiv
- 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