Performance of AI agents based on reasoning language models on ALD process optimization tasks

In this work, we explore the performance and behavior of AI agents based on reasoning large language models on atomic layer deposition (ALD) process optimization tasks. In these tasks, an agent has to iteratively explore a process configuration space to identify the optimal dose times for the precursor and the coreactant, generally without any prior knowledge about the process, including whether it is actually self-limited. The agent is meant to interact iteratively with an ALD reactor in a fully unsupervised way, receiving feedback on the results of the proposed experiments. We evaluate this agent using a simple model of an ALD tool that incorporates ALD processes with different self-limited surface reaction pathways as well as a nonself-limited component. Our results show that agents based on reasoning models like OpenAI’s o3 and GPT5 consistently succeeded at completing this optimization task, with a performance on par or superior to that of previous machine learning approaches. However, we observed significant run-to-run variability due to the nondeterministic nature of the model’s response and search strategy. In order to understand the logic followed by the reasoning model, we captured the reasoning language model’s open response detailing the reasoning process. An analysis of the responses showed that the logic of the model was sound and that its reasoning was based on the notions of self-limited process and saturation expected in the case of ALD. However, the agent can sometimes be misled by its own prior choices when exploring the optimization space, which contributes to the variability of the results of the optimization process.