Structural optimization is a fundamental step in density functional theory (DFT) calculations, typically driven by the Broyden–Fletcher–Goldfarb–Shanno (BFGS) optimizer. However, the standard BFGS algorithm relies on a local quadratic approximation of the potential energy surface (PES), which frequently breaks down in highly non-quadratic regimes typical of complex surface adsorption systems and defective bulk materials. This breakdown leads to “Hessian pollution”, a phenomenon where higher-order anharmonicities introduce spurious off-diagonal inter-atomic couplings that distort curvature estimates and significantly stall convergence. Herein, we propose a physics-inspired algorithmic intervention to the BFGS method that systematically suppresses this pollution. Once the maximum residual force drops below a specific activation threshold (e.g., 0.5 or 0.1 eV/Å), our approach conditionally resets all off-diagonal Hessian blocks, and introduces an isotropic background stiffness strategy where these blocks can be repopulated with a small positive constant rather than zeroed completely. This balances the robust stability of diagonal dominance with accelerated convergence speed. Implemented as an add-on to the Atomic Simulation Environment (ASE) Library, the method is lightweight, transferable, and compatible with standard DFT codes. Tests across diverse chemical systems, including atomic and molecular adsorbates (O*, H*, CO*) on Pt(111) surfaces and defective bulk oxides (WO_3–x), demonstrate substantial reductions in the number of required force calls without biasing the final optimized geometry. It offers a practical tool for high-throughput DFT workflows that eliminates the need for domain-specific training. This method is available via our open-source package, Hessian-Engineered Relaxation Optimizer (HERO).

The Dynamic Database of Solid-State Electrolyte (DDSE) is an advanced online platform offering a comprehensive suite of tools for solid-state battery research and development. Its key features include statistical analysis of both experimental and computational solid-state electrolyte (SSE) data, interactive visualization through dynamic charts, user data assessment, and literature analysis powered by a large language model. By facilitating the design and optimization of novel SSEs, DDSE serves as a critical resource for advancing solid-state battery technology. This Technical Report provides detailed tutorials and practical examples to guide users in effectively utilizing the platform.

Magnesium (Mg) is a promising alternative to lithium (Li) as an anode material in solid-state batteries due to its abundance and high theoretical volumetric capacity. However, the sluggish Mg-ion conduction in the lattice of solidstate electrolytes (SSEs) is one of the key challenges that hamper the development of Mg-ion solid-state batteries. Though various Mg-ion SSEs have been reported in recent years, key insights are hard to be derived from a single literature report. Besides, the structure-performance relationships of Mg-ion SSEs need to be further unraveled to provide a more precise design guideline for SSEs. In this viewpoint article, we analyze the structural characteristics of the Mg-based SSEs with high ionic conductivity reported in the last four decades based upon data mining - we provide big-data-derived insights into the challenges and opportunities in developing next-generation Mg-ion SSEs.

All-solid-state batteries (ASSBs) are a class of safer and higher-energy-density materials compared to conventional devices, from which solid-state electrolytes (SSEs) are their essential components. To date, investigations to search for high ion-conducting solid-state electrolytes have attracted broad concern. However, obtaining SSEs with high ionic conductivity is challenging due to the complex structural information and the less-explored structure-performance relationship. To provide a solution to these challenges, developing a database containing typical SSEs from available experimental reports would be a new avenue to understand the structure-performance relationships and find out new design guidelines for reasonable SSEs. Herein, a dynamic experimental database containing > 600 materials was developed in a wide range of temperatures (132.40–1261.60 ​K), including mono- and divalent cations (e.g., Li⁺, Na⁺, K⁺, Ag⁺, Ca²⁺, Mg²⁺, and Zn²⁺) and various types of anions (e.g., halide, hydride, sulfide, and oxide). Data-mining was conducted to explore the relationships among different variates (e.g., transport ion, composition, activation energy, and conductivity). Overall, we expect that this database can provide essential guidelines for the design and development of high-performance SSEs in ASSB applications. This database is dynamically updated, which can be accessed via our open-source online system.