Promotor: dr. Nong Artrith
Employed since: September 2021
Room: 4th floor study area DDW
Understanding Solid-State Electrochemical Interfaces from Structure to Function through First-Principles Calculations and Machine Learning
Solid-state batteries are attracting worldwide interest as one of the modern technologies for safe batteries with high energy density. Substituting flammable liquid electrolytes used in conventional Li-ion batteries by solid electrolytes is considered a key strategy for improving safety and to facilitate the use of high-energy Li-metal anodes.1 However, the design or discovery of solid electrolytes that provide not only sufficiently good ionic conductivity but are also compatible with energy-dense electrodes has remained a great challenge.
In actual solid-state batteries, solid electrolytes typically react with the electrodes at the interfacial layers and form a solid electrolyte interphase (SEI) at the negative electrode and a cathode electrolyte interface (CEI) at the positive electrode. The SEI and CEI can give rise to high resistance and low mechanical or electrochemical stability, leading to poor battery performance.2 The atomic structures of the SEI and CEI are usually complex non-crystalline and evolve dynamically during charge-discharge cycling. A better understanding of the properties of the SEI/CEI and related phenomena, e.g., reduced Li-ion diffusion and electrochemical degradation, is crucial to the rational design of better solid-state batteries.
This research aims to improve the understanding of CEI structures and related properties at the atomic level, which determine the macroscopic properties of solid-state batteries. Machine learning potentials (MLP) will be trained on structure-energy relationships from a dataset of atomistic calculations based on density functional theory.3-7 These MLPs will be used to construct realistic atomic-scale models of CEI structures and to study Li-ion dynamics and electrochemical reactions that occur at the interfaces during cycling using our machine-learning software package ænet (http://ann.atomistic.net).3
1. R. Chen, H. Li,et al. Chem. Rev. 120, 14, 6820–6877 (2020).
2. Y. Xiao, G. Ceder, et al. Nat. Rev. Mater. 5, 105–126 (2020)
3. N. Artrith and A. Urban, Comput. Mater. Sci. 114, 135-150 (2016).
4. N. Artrith, J. Phys. Energy 1, 032002 (2019).
5. H. Guo, N. Artrith, et al. Front. Energy Res. 9,695902 (2021).
6. T. Morawietz and N. Artrith J. Comput. Aided Mol. Des. 2, 031001 (2021).
7. N. Artrith, K.T. Butler, F.X. Coudert, S. Han, O. Isayev, A. Jain, A. Walsh, Nat. Chem. 13, 505-508 (2021).
September 2021 – present
PhD Candidate in Materials Chemistry and Catalysis (MCC) Group, Debye Institute for Nanomaterials Science, Utrecht University, The Netherlands
August 2018 – August 2021
Master’s degree in Physics, Institute of Science, Suranaree University of Technology, Nakhon Ratchasima, Thailand.
Project: Role of Sn doping on the improved electronic conductivity of V2O5 cathode for Li-ion batteries: an atomistic model
August 2014 – May 2018
Bachelor’s degree in Physics, Department of Science, Khon Kaen University, Khon Kaen, Thailand. Project: Understanding structural properties of Na-doped LiFePO4 by First-principles calculation