计算成本减半，化学反应发现工具ChemOntology将人类直觉「编码」到系统中，加速反应路径搜索

Core Insights - The ChemOntology framework developed by Hokkaido University represents a significant advancement in chemical ontology, transitioning from descriptive annotation to prescriptive control, demonstrating its effectiveness in accelerating reaction path searches and highlighting the potential of integrating chemical knowledge with automated computation [1][2]. Group 1: Framework and Methodology - ChemOntology is a knowledge-driven computational framework that integrates chemical reaction rules, structural constraints, and quantum chemical path search processes, allowing efficient exploration of reaction paths without relying on large-scale data training [7][14]. - The framework utilizes the Artificial Force Induced Reaction (AFIR) as a computational engine, explicitly encoding chemical knowledge to guide search directions and filter out irrelevant structures in real-time [12][14]. - The methodology is validated through the classic Heck reaction, which involves complex mechanisms and multiple key steps, showcasing the framework's ability to identify critical intermediates and distinguish between main and side reaction pathways [6][15]. Group 2: Experimental Results - The integration of ChemOntology with AFIR allows for a significant reduction in computational costs, achieving effective results with nearly half the number of paths explored compared to traditional methods, thus lowering overall computational expenses by approximately 50% [4][20]. - The experimental results indicate that ChemOntology can generate clearer reaction pathways, effectively distinguishing between main products and side products, and significantly reducing the proportion of ineffective nodes in the reaction network [18][20]. Group 3: Industry Applications and Innovations - The fusion of chemical ontology with automated reaction path searching is creating a crucial bridge between theoretical chemistry and industrial applications, facilitating a shift from reactive analysis to proactive guidance in reaction mechanism research [21][23]. - Companies like Schrödinger and BASF are leveraging these advancements to automate reaction workflows and enhance catalyst development, demonstrating the practical value of integrating chemical ontology with quantum chemistry and AI [22][23].