报告人：欧阳润海教授上海大学

报告题目：多源实验数据机器学习寻找准确催化描述符

报告时间：2022年10月21日下午14:00

腾讯会议ID：298-859-714

报告摘要：

While considerable amount of experimental catalytic data are available in literature and databases, the inconsistency between different sources often impede the learning of accurate models. In this talk, I will introduce our recent method sign-constrained multi-task learning as implemented in the SISSO framework, termed SCMT-SISSO, for distilling accurate descriptors from experimental data in the example of predicting the catalytic activity of perovskite oxides for oxygen evolution reaction (OER). While many previous descriptors for the OER activity were proposed based on respective small datasets, we obtained the new 2D descriptor (dB, nB) with greatly improved universality and predictive accuracy based on 13 experimental datasets from different publications. This descriptor allowed us to identify hundreds of unreported highly active perovskites from a large chemical space. Experiments were performed on several of the candidates, which confirmed two new perovskites that are highly active (>BSCF5582) for OER.

个人简介 ：

欧阳润海，上海大学特聘副研究员、PI、博士生导师。博士毕业于中科院大连化物所理论催化课题组，师从李微雪教授。先后在澳大利亚悉尼大学、美国加州大学河滨分校、德国马普FHI研究所理论部从事博士后研究，开发了结合符号回归和压缩感知的新方法SISSO。目前课题组主要聚焦于数据驱动材料发现的研究，包括在SISSO框架内的功能拓展以应用于各种复杂场景、机器学习(催化)材料研发、以及其它.数据驱动相关课题。

联系人：程涛 教授