PhD:ADVANCING LITHIUM-ION BATTERIES: AI-DRIVEN DISCOVERY OF Full-time

About UM6P:

Mohammed VI Polytechnic University (UM6P) is a Moroccan non-profit private research university located in the Green City of Benguerir, with a mission to promote education, training, and teaching, as well as applied research and innovation. UM6P boasts state-of-the-art infrastructure and an extensive academic and research network and is dedicated to economic and human development. The university places research and innovation at the service of the African continent and is committed to providing relevant skills training to advance Morocco and Africa’s innovation ecosystem. UM6P’s unique partnership approach strengthens its position as an avant-garde in these areas, and the university aims to shine nationally, continentally, and internationally as a leading institution for education and research. UM6P offers highly competitive incentives to its current and future talented research staff.

Entity: LIMSET (UM6P)

Thesis Subject Title: ADVANCING LITHIUM-ION BATTERIES: AI-DRIVEN DISCOVERY OF HIGH-PERFORMANCE SOLID STATE ELECTROLYTES

Thesis Subject Description:

• Context: The advancement of computational models and artificial intelligence (AI) algorithms represents a critical aspect of this research endeavor. While existing studies [1-4] have contributed valuable insights, the refinement and adaptation of these tools to capture the multifaced properties of solid-state electrolytes are necessary to ensure the relevance and applicability of this project. Specifically, the AI models and algorithms used in current research efforts can be improved to accurately capture properties such as structural arrangement, thermodynamic stability, ionic conductivity, chemical reactivity, and mechanical properties. This entails the development of sophisticated techniques to simulate and analyze new structural configurations, model ion transport mechanisms, predict chemical reactions, and assess mechanical integrity. Addressing these computational challenges requires interdisciplinary collaboration and the utilization of high-performance computing resources. By advancing computational models and algorithms, this research aims to facilitate the rational design and optimization of electrolyte materials for next-generation energy storage technologies. However, overcoming these challenges requires innovative approaches and collaborative efforts to drive progress in the field.

• Research Objectives: Unlocking the full potential of lithium-ion batteries (LIBs) requires innovative materials that push the boundaries of energy storage. This PhD proposal aims to deal with LIBs technologies through the combination of the power of artificial intelligence and computational materials science. By addressing the current challenges in solid-state electrolytes (SSEs), the proposal seeks to accelerate the identification and optimization of such materials being used in batteries, contributing to the development of safer, more efficient, and longer-lasting energy storage solutions. Through a systematic approach encompassing database acquisition, computational screening, machine learning, and experimental validation, this research endeavors to overcome current challenges in LIBs and contribute to the development of next generation energy storage systems.

• Admission Criteria: The position requires a master’s degree or equivalent from a university, with a preference for candidates experienced in computational techniques and artificial intelligence, along with knowledge of experimental characterization and battery technology. Proficiency in material synthesis, collaborative project work, scientific writing, research, analytical skills, strong communication (written and verbal) in English and French, analytical mindset, innovation, and excellent interpersonal skills are essential.

• References:
  [1] Holland J, Demeyere T, Bhandari A, Hanke F, Milman V, Skylaris CK. A Workflow for Identifying Viable Crystal Structures with Partially Occupied Sites Applied to the Solid Electrolyte Cubic Li7La3Zr2O12. The Journal of Physical Chemistry Letters. 2023 Nov 8.
• [2] Jalem, Randy, et al. “Multiobjective Solid Electrolyte Design of Tetragonal and Cubic Inverse-Perovskites for AllSolid-State Lithium-Ion Batteries by High-Throughput Density Functional Theory Calculations and AI-Driven Methods.” The Journal of Physical Chemistry C 127.35 (2023): 17307-17323.
• [3] Kang S, Kim M, Min K. Discovery of Superionic Solid-State Electrolyte for Li-Ion Batteries via Machine Learning.The Journal of Physical Chemistry C. 2023 Sep 21;127(39):19335-43.
• [4] Sun J, Kang S, Kim J, Min K. Accelerated discovery of novel garnet-type solid-state electrolyte candidates via machine learning. ACS Applied Materials & Interfaces. 2023 Jan 19;15(4):5049-57.