Combination study of high throughput screening techniques and artificial intelligence (AI) to identify innovative materials for next generation of battery

  • Artificial Intelligence & data intelligence,
  • phD
  • CEA en Région
  • Grenoble
  • Level 7
  • 2024-09-01
  • YILDIRIM Gunay (DRT/DAQUIT (CTReg)//Autre)

In recent years, the CEA has set up an experimental high-throughput screening (HTS) activity for lithium battery materials, based on combinatorial synthesis by sputtering and various high-throughput characterisation techniques on large substrates (typically 4 inches). Optimisation of material compositions is traditionally carried out by analysing experimental designs. In the framework of this thesis, we propose to compare the results of this conventional method with the Artificial Intelligence tools developed at CEA-LIST (symbolic AI) and CEA-CTREG (connectionist AI). The objetive is to demonstrate that AI can advantageously replace standard experimental design in order to offer an innovative, high-performance high-throughput screening tool.

Ingénieur Intelligence Artificielle, Machine Learning et Big Data


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