Combining the Power of High-Performance Computing, High-Throughput Ab Initio Calculations, and Machine Learning towards Materials Informatics
DescriptionThe 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, high‑throughput ab initio calculations and machine learning. I will briefly introduce the OPTIMADE API [*] 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 illustrate the power of materials informatics through a few recent examples.
[*] C.W. Andersen et al., Sci. Data 8, 217 (2021).
TimeTuesday, June 2716:30 - 17:00 CEST