报告题目：Combining the Power of High-Throughput Ab Initio Calculations and Machine Learning towards Materials Informatics
报告人：Gian-Marco Rignanese教授 1UCLouvain, Belgium; 2西北工业大学
报告时间：2024年1月11日10:00
报告地点：物理科技楼409
报告摘要：The progress in first-principles simulation 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 (e.g., lithium battery and photovoltaic). As a result, a number of databases have also become available online, providing access to various properties of materials, mainly ground‑state though. Indeed, for more complex properties (e.g., linear or higher‑order responses), the HT approach is still out of reach because of the required CPU time. To overcome this limitation, machine learning approaches have recently attracted much attention in the framework of materials design. In this talk, I will review recent progress in the emerging field of materials informatics. I will briefly introduce the OPTIMADE API [1] that was developed for searching the leading materials databases (such as AFLOW, the Materials Cloud, the Materials Project, NOMAD, OQMD, ...) using the same queries. I will introduce the MODNet framework and its recent developments for predicting materials properties [2-5]. This approach, which is particularly well suited for limited datasets, relies on a feedforward neural network and the selection of physically meaningful features. Finally, I will illustrate the power of materials informatics, combining high‑throughput ab initio calculations and machine learning, through a few recent examples.
References
[1] C.W. Andersen et al., Sci. Data 8, 217 (2021).
[2] P.‑P. De Breuck, G. Hautier, and G.‑M. Rignanese, npj Comput. Mater. 7, 83 (2021).
[3] P.‑P. De Breuck, M.L. Evans, and G.‑M. Rignanese, J. Phys.: Condens. Matter 33, 404002 (2021).
[4] P.‑P. De Breuck, G. Heymans, and G.‑M. Rignanese, J. Mater. Inf. 2, 10 (2022).
[5] X. Liu, P.‑P. De Breuck, L. Wang, and G.‑M. Rignanese, npj Comput. Mater. 8, 233 (2022).
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 where is now part-time Full Professor, and at the F.R.S.-FNRS where he is now Research Director. Since 2022, he is also Associate Professor at the Northwestern Polytechnical University at Xi’an (China). He plays an active role in the development of different softwares (Abinit, Abipy, PyMatGen, FireWorks) and pseudopotentials through the PseudoDojo. He contributes to the Materials Project and is one of the key players of OPTIMADE consortium. More recently, he has introduced the MODNet machine-learning model for the prediction of materials properties. 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.
