Research Line: Computational Platforms and AI-aided Materials Discovery
We leverage high-performance computing, quantum computing, workflow automation, and data-driven approaches to accelerate the discovery and design of new materials.
| Research thrusts | Research priorities | Research foci |
|---|---|---|
| Workflow automation & data-driven discovery | • Workflow automation for materials modeling and experimental data analysis • High-throughput and data-driven materials discovery | Development of fully automated pipelines that connect simulation codes with experimental databases; ab-initio high-throughput screening combined with machine-learning models to rapidly pinpoint materials exhibiting target properties, accelerating the design–test loop. |
| Enabling advanced simulations | • Advanced simulation of linear and non-linear spectroscopies | Implementation of many-body and time-dependent methods (GW, BSE, TDDFT, etc.) in open-source scientific software to predict optical, IR and Raman, X-ray, and non-linear spectra, offering direct guidance for experimental analysis. |
| Tier-0 HPC & quantum computing | • Use of high-performance and quantum computers to carry out atomistic simulations | Leveraging exascale supercomputers and emerging quantum hardware to perform large-scale, high-accuracy atomistic simulations beyond classical computational limits. |