Efficiently Preparing Structure-Controllable High Entropy Anodes Via Computation-Guided Sintering in an Optimized Flash Furnace

Owing to their extensive design landscape and configurational entropy-driven structural stability, non-equimolar high entropy oxides (HEOs) emerged as a material of significant promise. Nevertheless, routine exploration and synthesis have not only proved to be laborious and time-inefficient but also impeded the capture of nonequilibrium phases. To cope with the challenge, we make efforts on two fronts. Primarily, a resistance adjustable furnace molded from boron nitride and graphite blend was established, not only provides flash thermal treatment up to 2000 degree celsius but also presents better temperature uniformity across the sample and hours of hightemperature durability, laying a solid foundation for efficient synthesis and structure regulation. Next, machine learning and multi-scale computation were applied to predict the target product and appropriate path, making the HEO synthesis with higher efficiency. In this work, a kind of spinel-type product (Fe 0.25 Co 0.21- Ni 0.21 Mn 0.21 Cr 0.12 ) 3 O 4 with lower discharging potential and reinforced cyclic stability was designed for lithium batteries. By taking advantage of the flash furnace and computation-guided conditions (time, temperature, and precursor), the product with desirable grain size and opportune surficial oxygen vacancies was precisely prepared through non-equilibrium fast sintering (the product was denoted as NEFS). Without any surface coating or complex morphology, NEFS maintains higher than 500 mAh g -1 (500 mA g -1 ) after 100 cycles and also represents better performances than the counterpart via conventional sintering. We expected that, under the guidance of advanced computation, the flash furnace holds great potential for more efficient and sophisticated synthesis.