# LLM Agents for Knowledge Discovery in Atomic Layer Processing

arXiv: https://arxiv.org/abs/2509.26201 · HTML: https://mlsanity.com/paper/2509.26201/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 57 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 8 of 8)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [ChemCrow: Augmenting large-language models with chemistry tools](https://mlsanity.com/paper/2304.05376/) (2023)
- [HoneyComb: A Flexible LLM-Based Agent System for Materials Science](https://mlsanity.com/paper/2409.00135/) (2024)
- [Measuring Scientific Capabilities of Language Models with a Systems Biology Dry Lab](https://mlsanity.com/paper/2507.02083/) (2025)
- [Benchmarking large language models for materials synthesis: the case of atomic layer deposition](https://mlsanity.com/paper/2412.10477/) (2024)
- [Machine learning and atomic layer deposition: predicting saturation times from reactor growth profiles using artificial neural networks](https://mlsanity.com/paper/2205.08378/) (2022)
- [Validation of the Scientific Literature via Chemputation Augmented by Large Language Models](https://mlsanity.com/paper/2410.06384/) (2024)

## Built on by (most cited first, 1 of 1)

- [Agentic AI Scientists Are Not Built For Autonomous Scientific Discovery](https://mlsanity.com/paper/2605.08956/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have garnered significant attention for several years now. Recently, their use as independently reasoning agents has been proposed. In this work, we test the potential of such agents for knowledge discovery in materials science. We repurpose LangGraph's tool functionality to supply agents with a black box function to interrogate. In contrast to process optimization or performing specific, user-defined tasks, knowledge discovery consists of freely exploring the system, posing and verifying statements about the behavior of this black box, with the sole objective of generating and verifying generalizable statements. We provide proof of concept for this approach through a children's parlor game, demonstrating the role of trial-and-error and persistence in knowledge discovery, and the strong path-dependence of results. We then apply the same strategy to show that LLM agents can explore, discover, and exploit diverse chemical interactions in an advanced Atomic Layer Processing reactor simulation using intentionally limited probe capabilities without explicit instructions.