Large Language Models (LLMs) have emerged as a transformative power in enhancing
natural language comprehension, representing a significant stride toward artificial general …

Recently, tool learning with large language models (LLMs) has emerged as a promising
paradigm for augmenting the capabilities of LLMs to tackle highly complex problems …

Reticular chemistry is the science of connecting molecular building units into crystalline
extended structures such as metal–organic frameworks and covalent organic frameworks …

The vast majority of materials science knowledge exists in unstructured natural language,
yet structured data is crucial for innovative and systematic materials design. Traditionally, the …

The emergence of artificial intelligence (AI) and machine learning (ML) in the field of drug
discovery has been propelled by significant advances in computer science, infrastructure …

Material discovery is a critical research area with profound implications for various
industries. In this work, we introduce MatExpert, a novel framework that leverages Large …

Artificial intelligence (AI) methods are poised to revolutionize intellectual work, with
generative AI enabling automation of text analysis, text generation, and simple decision …

Solving complex real-world tasks requires cycles of actions and observations. This is
particularly true in science, where tasks require many cycles of analysis, tool use, and …

The advancement of artificial intelligence algorithms has expanded their application to
several fields such as the biomedical domain. Artificial intelligence systems, including Large …

To enhance large language models (LLMs) for chemistry problem solving, several LLM-
based agents augmented with tools have been proposed, such as ChemCrow and …