6 papers
DrugMCTS: a drug repurposing framework combining multi-agent, RAG and Monte Carlo Tree Search
Zerui Yang, Yuwei Wan, Siyu Yan +4
Recent advances in large language models have demonstrated considerable potential in scientific domains such as drug repositioning. However, their effectiveness remains constrained…
CheMatAgent: Enhancing LLMs for Chemistry and Materials Science through Tree-Search Based Tool Learning
Mengsong Wu, YaFei Wang, Yidong Ming +7
Large language models (LLMs) have recently demonstrated promising capabilities in chemistry tasks while still facing challenges due to outdated pretraining knowledge and the diffic…
DARWIN 1.5: Large Language Models as Materials Science Adapted Learners
Tong Xie, Yuwei Wan, Yixuan Liu +8
Materials discovery and design aim to find compositions and structures with desirable properties over highly complex and diverse physical spaces. Traditional solutions, such as hig…
Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model
Yanpeng Ye, Jie Ren, Shaozhou Wang +6
Knowledge in materials science is widely dispersed across extensive scientific literature, posing significant challenges to the efficient discovery and integration of new materials…
ByteScience: Bridging Unstructured Scientific Literature and Structured Data with Auto Fine-tuned Large Language Model in Token Granularity
Tong Xie, Hanzhi Zhang, Shaozhou Wang +5
Natural Language Processing (NLP) is widely used to supply summarization ability from long context to structured information. However, extracting structured knowledge from scientif…
From Tokens to Materials: Leveraging Language Models for Scientific Discovery
Yuwei Wan, Tong Xie, Nan Wu +3
Exploring the predictive capabilities of language models in material science is an ongoing interest. This study investigates the application of language model embeddings to enhance…