Abstract: Advancements in machine learning and artificial intelligence that leverage scientific data are transforming materials discovery. Despite the wealth of existing scientific literature, the availability of both structured experimental data and chemical domain knowledge that can be easily integrated into data-driven workflows is limited. The motivation to integrate this information, as well as additional context from first-principle calculations and physics-informed deep learning surrogate models, is to enable efficient exploration of the relevant chemical space and to predict structure-property relationships of new materials $\textit{a priori}$. Ultimately, such a framework could replicate the expertise of human subject-matter experts.In this work, we present a chemist AI agent, powered by large language models (LLMs), that discovers new compounds with desired properties via inverse design (property-to-structure). In specific, the agent leverages domain-specific insights from foundational scientific literature to propose new materials with enhanced chemical properties, iteratively evaluating them using relevant surrogate models in a rational design process, while accounting for design constraints.The model supports both closed-loop and human-in-the-loop feedback cycles with property prediction, validation, and uncertainty assessment.We demonstrate the flexibility of this agent by applying it to various materials target properties including surfactants, ligand and drug candidates, and metal-organic frameworks. Our approach holds promise to both accelerate the discovery of new materials and enable the targeted design of materials with desired functionalities.The methodology is available as an open-source software on https://github.com/mehradnas92/dZiner