Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

Recent developments in foundation models, like Large Language Models (LLMs) and Vision-
Language Models (VLMs), trained on extensive data, facilitate flexible application across …

Machine learning (ML) using data sets of atomic and molecular force fields (FFs) has made
significant progress and provided benefits in the fields of chemistry and material science …

The rapid emergence of large language model (LLM) technology presents promising
opportunities to facilitate the development of synthetic reactions. In this work, we leveraged …

Recent advancements in artificial intelligence and automation are transforming catalyst
discovery and design from traditional trial-and-error manual mode into intelligent, high …

The emergence of specialized large language models (LLMs) has shown promise in
addressing complex tasks for materials science. Many LLMs, however, often struggle with …

While abstraction is critical for the transferability of automated laboratory science in (bio)
chemical and materials sciences, its improper implementation is a technical debt taken …

Material science has historically evolved in tandem with advancements in technologies for
characterization, synthesis, and computation. Another type of technology to add to this mix is …

Recently, Multimodal Large Language Models (MLLMs) achieve remarkable performance in
numerous zero-shot tasks due to their outstanding cross-modal interaction and …

Intelligent agents powered by large language models (LLMs) have demonstrated substantial
promise in autonomously conducting experiments and facilitating scientific discoveries …