The AtomicLimits Database: Towards Artificial Intelligence as a Research Assistant for Atomic Layer Deposition and Etching

Abstract

The AtomicLimits ALD/ALE database is a community resource that catalogues atomic layer deposition (ALD) and atomic layer etching (ALE) processes reported in the literature. While widely used, it currently captures only a small fraction of the available information, with limited process information per entry, and relies on manual curation. As ALD/ALE process development grows in demand and complexity, with many interdependent parameters and increasingly sophisticated chemistries such as for multicomponent films, decades of experimental and simulation knowledge remain locked across publications, supplementary files, and lab notebooks. Recent advances in artificial intelligence (AI), particularly machine learning (ML) and large language models (LLMs), offer a path to unlock this knowledge for data-driven analysis and process design, but realizing this potential depends on structured, machine-readable data. In this perspective, we propose a framework to evolve AtomicLimits into an AI-ready knowledge infrastructure that integrates literature-extracted, experimental, and simulation data through a community-defined schema, a structured template that specifies which data fields to capture and how to organize them. We outline the AI capabilities this enables, from structured querying to autonomous experimentation, and discuss the coordinated community effort required to realize this vision for the atomic-scale processing field.