Large Language Models (LLMs) show strong cross-domain performance, further enhanced via fine-tuning on domain-specific data. While they have key breakthroughs, LLMs still struggle with complex tasks like accelerating chemical research–this needs integrating intensive environmental interactions (high-throughput experiments), sophisticated theoretical simulations (wavefunction- and density functional theory-based calculations), and reasoning (data analysis, optimization direction identification). To date, the most effective method is LLMs collaborating with existing chemical toolkits. Yet augmenting LLMs with these toolkits is challenging: they need diverse inputs, generate heterogeneous outputs, and raise LLM adaptation training costs. To tackle this, we propose SiMiao, a chemistry research assistant platform integrating LLMs (fine-tuned on chemical datasets) with chemistry-specific toolkits. SiMiao outperforms mainstream general-purpose and domain-specific models in chemical tasks, including advanced knowledge QA, literature understanding, molecular understanding, and scientific knowledge deduction. Consequently, it supports professional chemists, lowers barriers for non-experts, bridges theoretical-experimental chemistry gaps, and drives scientific progress. The official platform access link is accessible at https://ai4s.iflytek.com/chat.