Self-driving laboratories can explore synthesis conditions autonomously, but their decision-making layer is typically a black-box optimizer, and the output is a set of optimized samples, with the meas...
arXiv:2508.05427v2 Announce Type: replace
Abstract: Large language models (LLMs) are beginning to reshape how organic-synthesis workflows are represented, queried, planned, and connected to experimen...
By Kartar Kumar, Rajesh Kumar, Nikesh Lagun
The article reviews methods for assessing large language model (LLM) based AI agents in materials synthesis, focusing on their integration with experimental tools. It outlines evaluation strategies—including knowledge, reasoning, tool‑use, and closed‑loop benchmarks—and applies them to atomic layer deposition (ALD) as a case study. A practical framework for evaluating LLMs in this context is also presented.
By Angel Yanguas-Gil
The Perspective reviews the rapid growth of agentic AI systems in computational chemistry, noting an increase from a handful in 2024 to about fifty by August 2026. These systems are evolving from assisting with specific tasks to autonomously designing, executing, and analyzing in‑silico experiments, even drafting manuscripts. While fully autonomous AI scientists are not yet realized and human oversight remains, the trend toward commoditized generalist agents suggests a future where specialized systems may become obsolete, prompting reflection on the field’s direction and priorities.
By Pavlo O. Dral, Hassan Nawaz, Arif Ullah
arXiv:2608. 07454v1 Announce Type: cross Abstract: The total synthesis of a complex molecule is among the most demanding intellectual and experimental feats in chemistry: a chemist must plan many steps ahead for how to assemble simple building blocks into an intricate target, devise backup strategies, and anticipate procedural challenges.
By Daniel Armstrong, Xuan-Vu Nguyen, Octavian Susanu, Gabriel Gibberd, Th\'eo A. Neukomm, Tadd\"aus Strunden, Dan Forster, Morgane Delattre, Shawn Teh, Cl\'ement Rols, John Federice, Hayden Leatherwood, M. Lavelle Barnes, Maarten R. Dobbelaere, Peter Wipf, Jon T. Njardarson, Jieping Zhu, Philippe Schwaller
The article introduces the concept of agentic programs—scientific software that blends deterministic algorithms with bounded large‑language‑model (LLM) judgment, task‑specific verification, episodic maturation, and full delegation in production. It argues that recent LLM‑based agents enable this new form of computational materials science software. The authors illustrate the idea with DeMARS, an agentic program designed to build atomistic models from experimentally measured disordered crystal structures.
By Yunsung Lim, Haekwan Jeon, Jaesun Kim, Jisu Kim, Seungwu Han