EXTENDED ABSTRACT: With the growing demand for energy storage systems, alkali metal-ion batteries are receiving signiffcant attention, and the design of high-performance anode materials is critical. In this report, we present two promising anode materials for potassium-ion batteries—sulfur-functionalized MXene (S-MXene) and boron-doped graphene (BDG)— using density functional theory (DFT) calculations and machine learning predictions. Experimentally synthesized S-MXene exhibits metallic properties and structural integrity, with DFT results indicating strong potassium adsorption and low diffusion barriers, making it a promising anode candidate. Due to MXene's complex surface chemistry, we propose a fully frozen transfer learning framework to guide experimental control over surface groups and energy storage properties. For BDG, with a calculated potassium storage capacity of 854 mAh/g, random doping concentrations create a vast structure space. Given the importance of work function as a key descriptor for alkali ion storage, we employ a machine learning approach using a crystal graph convolutional neural network to predict work functions for over 560,000 structures. The selected targets were studied as anodes for lithium/sodium/potassium ion batteries, showing a high theoretical speciffc capacity of 2262/2546/1131 mAh/g. In summary, DFT and machine learning methods have played a key role in accelerating the design of new high-performance anode materials.
Keywords: MXene; high-capacity; anode materials; machine learning; ffrst-principle calculation
REFERENCES:
[1] H. Chen, X. Niu, Appl. Surf. Sci. 546 (2021) 149096
[2] C. Wei, X. Niu, H. Chen, Mater. Res. Express 9 (2022) 065604
[3] Y. Luo, H. Chen, X. Niu. Phys. Chem. Chem. Phys. 25 (2023) 12200-12206
[4] Z. Song, X. Niu, H. Chen. Phys. Chem. Chem. Phys. 25 (2024) 14847-14856
Professor Niu received his PhD in materials science from the UCLA in 2008. He is currently a professor at the University of Electronic Science and Technology of China, director of the UESTC-Changhong New Energy Materials and Devices Joint Laboratory, and a fellow of the Royal Society of Chemistry. His main research work is the controllable growth and physical properties of low-dimensional materials and their heterostructures, and their applications in the ffelds of energy conversion and storage and optoelectronic information devices. He currently publishes more than 100 papers in physics and materials academic journals such as Phys. Rev. Lett., Joule, Mater. Today, Adv. Mater.