Université catholique de Louvain

报告人简介：

Gian-Marco Rignanese received his Engineering degree from the Université catholique de Louvain in 1994 and Ph.D. in Applied Sciences from the Université catholique de Louvain in 1998. During his Ph.D., he also worked as a Software Development Consultant for the PATP (Parallel Application Technology Project), collaboration between CRAY RESEARCH and Ecole Polytechnique Fédérale de Lausanne (EPFL) in the group of Prof. Roberto Car. He carried his postdoctoral research at the University of California at Berkeley in the group of Prof. Steven Louie. In 2003, he obtained a permanent position at the Université catholique de Louvain. In 2022, he was appointed as Adjunct Professor at the Northwestern Polytechnical University in Xi'an (China). In 2019, he was named APS Fellow for original efforts developing free license software in the field of electronic structure calculations, and high-throughput calculations in a broad range of materials types.

报告摘要：

The progress in first-principles codes and supercomputing capabilities have given birth to the so-called high-throughput (HT) ab initio approach, thus allowing for the identification of many new compounds for a variety of applications. A number of databases have thus become available online, providing access to properties of materials, mainly groundstate though. Indeed, for more complex properties (e.g., linear responses), the HT approach is still problematic because of the required CPU time. To overcome this limitation, machine learning approaches have recently attracted much attention.

In this talk, I will review recent progress in materials informatics focusing on the response properties of inorganic materials which play of key role in various physical phenomena such as linear and non-linear optics, thermal conductivity, superconductivity, or ferroelectricity. I will first present our HT calculations of the response properties based on density functional perturbation theory. I will briefly introduce the OPTIMADE API that was developed for searching the leading materials databases using the same queries. Finally, I will review the MODNet framework for predicting materials properties and which is particularly well suited for limited datasets through the selection of physically meaningful features.

邀请人：翁红明（8264 9941）

联系人：胡  颖（8264 9361）