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2022/05 I gave an invited talk about “Forward and Inverse Design of Solid Materials with Graph Neural Networks” at MIT GraphEx 2022 symposium.
2022/05 I co-organized “Recent Advances in Data-Driven Discovery of Materials for Energy Conversion and Storage” Symposium at MRS Spring 2022 in Honolulu.
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.
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.
2020/07 I defended my PhD thesis. A video can be found in YouTube and the thesis will be available at MIT library soon. Update (2021/01): thesis is now available at MIT library. Update (2021/07): slides is available here
2019/06 Our work “Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials” is published on Nature Communications.
2019/02 I joined DeepMind in London as an intern to start a project at the science team.
2019/02 I joined X (formerly Google X) in Mountain View as an intern to work on an early stage project that combines machine learning and physics.