Kyoungmin Min

First-principles calculations have been used to investigate the effects of Al and Mg doping on the prevention of degradation phenomena in Li(Ni_0.8Co_0.1Mn_0.1)O₂ cathode materials. Specifically, we have examined the effects of dopants on the suppression of oxygen evolution and cation disordering, as well as their correlation. It is found that Al doping can suppress the formation of oxygen vacancies effectively, while Mg doping prevents the cation disorde

Mechanical degradation phenomena in layered oxide cathode materials during electrochemical cycling have limited their long-term usage because they deteriorate the structural stability and result in a poor capacity retention rate. Among them, intra-granular cracking inside primary particles progressively degrades the performance of the cathode but comprehensive understanding of its intrinsic origin is still lacking. In this study, the mechanical properties of the primary particle in a Ni-rich lay

The synergistic effect of Na⁺/Al³⁺ dual doping is investigated to improve the structural stability and electrochemical performance of LiNi_0.88Co_0.08Mn_0.04O₂ cathodes for Li-ion batteries. Rietveld refinement and density functional theory calculations confirm that Na⁺/Al³⁺ dual doping changes the lattice parameters of LiNi_0.88Co_0.08Mn_0.04O₂. The changes in the lattic

For developing the industrially feasible Ni-rich layered oxide cathode with extended cycle life, it is necessary to mitigate both the mechanical degradation due to intergranular cracking between primary particles and gas generation from the reaction between the electrolyte and residual Li in the cathode. To simultaneously resolve these two issues, we herein propose a simple but novel method to reinforce the primary particles in LiNi_0.91Co_0.06Mn_0.03O₂ b

Predicting both accurate and reliable solubility values has long been a crucial but challenging task. In this work, surrogated model-based methods were developed to accurately predict the solubility of two molecules (solute and solvent) through machine learning and deep learning. The current study employed two methods: (1) converting molecules into molecular fingerprints and adding optimal physicochemical properties as descriptors and (2) using graph convolutional network (GCN) models to convert

Ni-rich layered oxides are promising cathode materials due to their high capacities. However, their synthesis process retains a large amount of Li residue on the surface, which is a main source of gas generation during operation of the battery. In this study, combined with simulation and experiment, we propose the optimal metal phosphate coating materials for removing residual Li from the surface of the Ni-rich layered oxide cathode material LiNi_0.91Co_0.06Mn_0.03O

The effect of bi-functional coatings consisting of Zr and phosphate (P) on the electrochemical performance of Li_1.0Ni_0.8Co_0.15Mn_0.05O₂ (NCM) has been investigated. The presence of various types of Zr and P compounds such as oxides (ZrO₂ and Li₂ZrO₃) and phosphates (Zr₂P₂O₉, ZrP₂O₇ and LiZr₂(PO₄)₃) in the coating was confir

Optimizing synthesis parameters is the key to successfully design ideal Ni-rich cathode materials that satisfy principal electrochemical specifications. We herein implement machine learning algorithms using 330 experimental datasets, obtained from a controlled environment for reliability, to construct a predictive model. First, correlation values showed that the calcination temperature and the size of the particles are determining factors for achieving a long cycle life. Then, we compared the ac

The necessity of developing Ni-rich layered oxides cathode materials with more than 90% of Ni content is rapidly increasing for satisfying the demand of achieving the high capacity of Li-ion batteries. However, including more Ni contents results in increased formation of undesirable Li residues at the surface as well as deteriorating several types of degradation behaviors during cycling, which are the critical factors of design rules for the cathode material. In this study, the facile synthesis

The ultimate goal of various fields is to directly generate molecules with desired properties, such as water-soluble molecules in drug development and molecules suitable for organic light-emitting diodes or photosensitizers in the field of development of new organic materials. This study proposes a molecular graph generative model based on an autoencoder for the de novo design. The performance of the molecular graph conditional variational autoencoder (MGCVAE) for generating molecules with speci

Li-ion solid-state electrolytes (SSEs) have great potential, but their commercialization is limited due to interfacial contact stability issues and the formation and growth of dendrites. In this study, a machine learning regression algorithm was implemented to screen for mechanically superior SSEs among 17,619 candidates. Elasticity information (14,238 structures) was imported from an available database, and their machine learning descriptors were constructed using physiochemical and structural

All-solid-state batteries (ASSBs) have attracted considerable attention because of their higher energy density and stability than conventional lithium-ion batteries (LIBs). For the development of promising ASSBs, solid-state electrolytes (SSEs) are essential to achieve structural integrity. Thus, in this study, a machine-learning-based surrogate model was developed to search for ideal garnet-type SSE candidates. The well-known Li₇La₃Zr₂O₁₂ structure wa