【学术视频】机器学习-计算化学Workshop | 麻省理工学院谢天

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图 | 谢天

题   目：Tutorials for CGCNN and GDyNets

报告人：谢天

单   位：Massachusetts Institute of Technology

时   间：2019-09-05

地   点：厦门大学化学化工学院

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报告摘要

The exponential increase of open materials data in the past few years poses a challenge to the community to efficiently utilize them for designing better materials. In this talk, I will present our recent works showcasing how graph neural networks can be used as a general framework to utilize large quantities of material data. We will first define the key architecture of crystal graph convolutional neural networks (CGCNN) to represent an arbitrary atomic structure. Then, we will demonstrate how to achieve state-of-the-art performance in predicting material properties and extract material design knowledge from the networks using this framework. Further, we will show several examples of applying this approach to the design of lithium-ion batteries. Finally, we will present our latest work that extends this approach to learn from molecular dynamics simulation trajectories without supervision and understand lithium-ion transport mechanism. 

个人简介

Tian Xie is a PhD student from the department of materials science and engineering at Massachusetts Institute of Technology, advised by Prof. Jeffrey Grossman. He received a B.S. in chemistry from Peking University in 2015. In 2019, he was a PhD software intern at X (formerly Google X), working on an early stage project combing machine learning and physics. After that, he joined DeepMind as a research intern. Tian’s research focuses on the development of machine learning algorithms for materials design, and he works closely with several groups from MIT and CMU to apply those algorithms to the discovery of materials for energy innovations. He developed CGCNN (Crystal Graph Convolutional Neural Networks) as a general approach to represent materials in 2017. 

会议简介

2019年9月3日-6日，由固体表面物理化学国家重点实验室（厦门大学）、福建省理论与计算化学重点实验室和厦门大学化学化工学院主办的“机器学习-计算化学Workshop”在厦门大学化学化工学院举办。本次Workshop邀请了相关领域的研究者报告领域前沿进展，并设置Hands-on tutorials环节帮助学员们熟悉代码的使用。此次Workshop的举办增进了不同领域研究者的交流，促进了开源共享的观念传递，希望推动大数据技术在计算化学和材料模拟等领域的应用。

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