Agentic inverse design of spatially programmed materials

Abstract

Spatially programmed functional materials, from functionally graded alloys and metamaterials to quantum superlattices, use their spatial structure to tailor physical properties. However, translating a desired spatial structure into a practical fabrication recipe remains challenging due to the complex relationship between processing conditions and resulting material structures. Here we show that a large language model (LLM) agent can inverse-design such fabrication recipes by using a physics-based simulator of ion-matter interactions as a tool. Given an arbitrary depth profile of implanted ions or vacancies, the agent selects the ion species, energies, incident angles, and target material layers; writes, runs, and interprets simulations; and iteratively refines the recipe until the simulated profile matches the target, with minimal human input. Our work demonstrates physics-guided agentic inverse design as a practical approach to making the spatial structure of materials a programmable variable, opening new opportunities to explore emergent physical phenomena and functional phases of matter.