SYSTEM AND METHODS FOR SCREENING, EVALUATING, AND OPTIMIZING THE STRUCTURE OF AMORPHOUS MOLYBENENSE SULFIDE ELECTRODE MATERIALS FOR ROOM TEMPERATURE SODIUM-SULFUR (Na-S) BATTERIES

Abstract

The invention relates to a system and method for screening amorphous molybdenum sulfide materials for sodium-sulfur batteries by combining deep learning modeling and density functional theory (DFT) computation. The system (100) includes a structural data generation and normalization module (110), a multi-stage screening module (120), a density functional computation module (130), and a data management and storage module (140). Candidate material structures are screened using deep learning modeling and validated by DFT computation to evaluate thermodynamic stability, phase stability, and electronic and electrochemical properties. The solution reduces computational costs, shortens time, and improves the efficiency of electrode material discovery for sodium-sulfur batteries.

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