The accurate description of noble metal hydrides remains a fundamental challenge for electronic structure theory, especially in systems involving heavy elements where relativistic effects and electron correlation are significant. In this study, we present a high-accuracy potential energy surface (PES) for the ${\rm AgAuH}^−$ anion, constructed from 3,595 UCCSD(T)-F12a energy points and fitted using a feedforward neural network with a root mean square error of 0.21 meV. The PES captures the entire configuration space, including linear and bent minima, transition-state-like structures, and dissociation pathways. Quantum vibrational bound states were computed using time-independent quantum dynamics, enabling detailed mode assignments. The high-fidelity PES and vibrational dataset were used to benchmark some widely employed density functional theory (DFT) methods, B3LYP, ωB97XD, XYG3, and XYGJ-OS. Among these, XYGJ-OS provided the best agreement with the reference data in terms of equilibrium geometries and vibrational frequencies. This study provides a robust benchmark for method development and validation in metal-containing systems and highlights the importance of using high-level reference data when modeling complex coinage-metal hydrides.

AI-driven reaction modeling in synthetic chemistry faces critical data gaps: the scarcity of mechanistic descriptors and inconsistent experimental protocols, leading models trained on sparse reactant-product pairs to falter in tasks like yield prediction, selectivity control, or condition optimization. Recent advances in mechanism-aware data curation, such as hybrid rule-ML frameworks and computational datasets, demonstrate progress but remain limited to small molecules (≤10 heavy atoms) or gas-phase approximations. Concurrently, robot-based highthroughput experimentation (HTE) platforms standardize protocols for a small number of reaction classes yet lack end-to-end traceability, often omitting workup and followed separation and purification steps. To bridge these gaps, we propose a closed-loop framework integrating computational chemistry, robotic HTE, and multimodal AI to resolve critical reaction modeling tasks. From the perspective of future work, the field necessitates expanded collaboration across the community to tackle complex systems, extend HTE to underrepresented reactions, and align data ontologies. Interdisciplinary collaboration is essential to transition from retrospective pattern recognition to mechanism-driven discovery, anchoring AI in datasets that encode why reactions succeed, not merely what products form.

In recent years, the rapid advancements in computer science have spurred the development of various cutting-edge intelligent algorithms. Among these, the transformer, which is built upon a multi-head attention mechanism, is one of the most prominent AI models. The advent of such algorithms has significantly advanced retrosynthesis prediction, though challenges remain in chemical interpretability and real-world deployment. Unlike traditional models, AI-based retrosynthesis prediction systems can automatically extract chemical knowledge from vast datasets to forecast retrosynthesis pathways. This review provides a comprehensive overview of modern intelligent algorithms applied to retrosynthesis prediction, with a particular focus on artificial intelligence techniques. We begin by discussing key deep learning models, then explore available chemical reaction datasets and molecular representations. The discussion extends to the latest state-of-the art in AI-assisted retrosynthesis models, including template-based, template-free, and semi-template-based approaches. Finally, we compare these models across various classifications, highlighting several challenges and limitations of current methods, and suggesting promising directions for future research.