talks & posters

A list of talks and poster presentations that I gave recently.

talks

2021

  • [Invited] Physics-based deep learning models for the discovery, understanding, and design of solid materials, DP Technology AI for energy material simulation series, in Chinese, online. [Video]
  • [Invited] Understanding the atomic scale dynamics in materials with unsupervised learning from molecular dynamics, MIT-IBM Watson AI Lab, online.

2020

  • [Spotlight Talk] Accelerate the screening of complex materials by learning to reduce random and systematic errors, NeurIPS ML4Molecules workshop, online. [Link] [Video]
  • Understanding the dynamical processes in materials with unsupervised learning from molecular dynamics, AI for Materials: From Discovery to Production, online. [Link]
  • Understanding Li-ion solvation dynamics with unsupervised learning from molecular dynamics, APS March Meeting, online. [Link]
  • [Thesis defense] Deep Learning Methods for the Design and Understanding of Solid Materials, MIT DMSE Doctoral Thesis Defense, online. [Link] [YouTube] [Slides]
  • [Invited] Physics Guided Neural Networks for the Design and Understanding of Materials, Aron Walsh’s group at Imperial College London, online.
  • [Invited] Physics Guided Neural Networks for the Design and Understanding of Materials, MIT IDSS/DMSE Special Seminar, online. [link]

2019

  • Understanding the atomic scale dynamics in materials with unsupervised learning from molecular dynamics, MRS Fall Meeting, Boston, MA. [Link] [PDF]
  • [Invited] High Throughput, Rapid Materials Design for Energy Storage, MIT Energy Initiative Annual Research Conference, Cambridge, MA.
  • [Invited] Graph neural networks as a general framework for the design and understanding of materials, Computational Chemistry and Machine Learning Workshop, Xiamen, China.
  • [Invited] Unsupervised Learning of Atomic Scale Dynamics in Materials, Toyota Research Institute, Mountain View, CA.

2018

  • CGCNN—A Graph Representation of Materials for Property Prediction and Materials Design, MRS Fall Meeting, Boston, MA. [Link] [PDF]
  • Continuous Representation of Chemical Environments for the Prediction of Material Properties, APS March Meeting, Los Angeles, CA. [Link]

2017

  • Graph Representation of Periodic Systems for Accurate and Explainable Prediction of Material Properties via Machine Learning, MRS Fall Meeting, Boston, MA. [Link]

posters

2020

  • Graph dynamical networks: unsupervised learning of atomic scale dynamics in materials, AI Powered Drug Discovery and Manufacturing Conference, Cambridge, MA. [Link] [PDF]

2018

  • CGCNN: a graph representation of materials for property prediction and materials design, MRL Materials Day, Cambridge, MA. [Link] [PDF] [Highlight]
  • Understanding Lithium-ion Transport with Deep Neural Networks, TRI Accelerated Materials Design & Discovery workshop, Redwood City, CA. [Link]
  • Artificial Intelligence for Accelerated Materials Design, MIT Intelligence Quest Launch, Cambridge, MA. [Link] [PDF]