Authors: Lisa H. Lu | Supratik Guha
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Agentic Synthesis: Autonomous Recipe Discovery via a System of AI Agents and Robotics: Electroplating Ni Thin Film on Cu Self-driving laboratories that couple robotics with AI agents for “closed-loop” experiments involving robot driven material synthesis, in-line measurement and decision-making without human intervention may change how materials are discovered and optimized. They hold the promise for automated materials development that can iterate significantly faster than manual workflows while reducing bias and human error. In this work we have explored the playbook for automated experiment–measure–decide loops in materials science using the technologically important example of electrochemical deposition of nickel. Nickel electroplating itself is a mature, industrially relevant process with well-documented chemistries and processes. Industrial Ni electroplating dates back to at least 1866 and has an estimated global market size of 3.9 Billion dollars in 2024. We have therefore chosen the electroplating of Ni on Cu substrates as an important model system for our proof-of-concept studies for benchmarking autonomous material synthesis discovery before tackling more complex chemistries. We present an autonomous experimental setup that discovers and refines electroplating recipes for depositing smooth Ni thin films on Cu substrates. The experiment demonstrates that AI-in-the-loop experimentation can efficiently navigate parameter spaces and uncover process windows for optimizing material performance without human intervention. Also, importantly, we show that hardware and software costs for such agentic synthesis have reduced to the point where they hold the promise of wide deployment and scalability. A benchtop robot executes a four-step closed loop: (1) film plating; (2) in-line measurement of roughness, coverage, and deposition rate; (3) AI feedback; and (4) plating under updated conditions. Decision-making is fully delegated to a large-language-model (LLM) ChatGPT-4o agent that proposes experimental settings and adapts them based on measured outcomes of previous experimental rounds to enable autonomous exploration. A photograph of the experiment setup is shown in the figure below. The controllable variables are plating current density, bath temperature, and electrolyte dilution (water-to-solution ratio), time in seconds. The original plating solution follows a Watts-type formulation: 24% nickel sulfate hexahydrate, 5% nickel chloride hexahydrate, 5% boric acid, 1.4% softener, 0.03% brightener, 0.3% wetting agent, and 64.37% water. The optimization target is multi-objective: maximize film smoothness and areal coverage while achieving a specified thickness. Thickness (and therefore deposition rate) is quantified by measuring the weight of the Ni plated Cu substrate before and after plating using a microbalance. Surface roughness is proxied by measuring the spot size of a laser beam reflected from the Ni surface, which is imaged using a camera. Larger spots indicate higher microroughness. Film coverage is estimated by microscope imaging. All of the mechanical operations are carried out by a robot arm and all hardware controlled via a Raspberry Pi. Analysis of the reflected laser spot and microscopic image is done by sending the images and appropriate text prompts to ChatGPT-4o used as a vision-language model. The LLM agent receives these metrics and textual guidance and returns the next set of parameters—(current density in A/cm 2 , dilution, temperature in °C, time in seconds)—in a machine-parsable format to drive the next robot loop. Operational safety is enforced by hard-coded limits of maximum plating voltage, current density, solution concentration, and solution temperature. We report autonomous experiments starting from an initial prompt to the LLM that describes the experimental objective (100 µm target thickness; smoothest, fully covered films). Though the LLM initially guessed sub-optimal deposition parameters, it consistently converged to optimal deposition parameters of: (a) keeping the original Watts solution (no or nearly no dilution); (b) 0.1 A/cm 2 plating current density (for comparison, current density recommendation is ~ 0.02 to 0.07 A/cm 2 range in the Nickel plating handbook); (c) 35 degree C solution temperature (highest within a manually set safety limit by us)--within 4 to 6 iterations. Our results successfully illustrates how co-designed AI-agentic and robotic systems can accelerate the discovery of material synthesis recipes. It may serve as a starting point toward autonomous optimization of more complex electrodeposition systems and electrochemical manufacturing workflows. Figure 1 Electroplating ; Workflow ; Robotics ; Process (computing) ; Thin film ; Plating (geology) ; Deposition (geology) Machine Learning in Materials Science ; Modular Robots and Swarm Intelligence ; Ammonia Synthesis and Nitrogen Reduction