Opportunities and Challenges of Machine Learning in Atomic Layer Deposition

The integration of machine learning methods is transforming many areas of research by, for instance, accelerating molecular dynamics simulations and enabling the improved prediction and optimization of chemical reactions. However, despite this progress, the adoption of data-driven approaches in atomic layer deposition (ALD) remains limited to a few pioneering studies. In this perspective, we take a first step toward closing this gap and bringing machine learning closer to ALD by introducing the key concepts of relevant algorithms and workflows, surveying the current literature, and outlining challenges and future directions for applying machine learning in ALD. We provide ideas on how the field can proceed to harvest the full potential of machine learning-based approaches for ALD which promises to enable precursor and material design as well as strongly improved computational and experimental approaches for atomic layer processing. This publication is licensed under You are free to share (copy and redistribute) this article in any medium or format and to adapt (remix, transform, and build upon) the material for any purpose, even commercially within the parameters below: Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. *Disclaimer This summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials. You are free to share (copy and redistribute) this article in any medium or format and to adapt (remix, transform, and build upon) the material for any purpose, even commercially within the parameters below: Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. *Disclaimer This summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials. You are free to share (copy and redistribute) this article in any medium or format and to adapt (remix, transform, and build upon) the material for any purpose, even commercially within the parameters below: Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. *Disclaimer This summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials.