33 citations · 70 across the 10 of their papers we have counts for
10 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models (LLMs) have recently demonstrated promising capabilities in chemistry tasks while still facing challenges due to outdated pretraining knowledge and the diffic…
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…
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…
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…
SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation
Yuwei Wan, Yixuan Liu, Aswathy Ajith +6
We introduce SciQAG, a novel framework for automatically generating high-quality science question-answer pairs from a large corpus of scientific literature based on large language…
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…
DARWIN Series: Domain Specific Large Language Models for Natural Science
Tong Xie, Yuwei Wan, Wei Huang +9
Emerging tools bring forth fresh approaches to work, and the field of natural science is no different. In natural science, traditional manual, serial, and labour-intensive work is…
Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT
Tong Xie, Yuwei Wan, Wei Huang +8
The amount of data has growing significance in exploring cutting-edge materials and a number of datasets have been generated either by hand or automated approaches. However, the ma…
Interdisciplinary Discovery of Nanomaterials Based on Convolutional Neural Networks
Tong Xie, Yuwei Wan, Weijian Li +7
The material science literature contains up-to-date and comprehensive scientific knowledge of materials. However, their content is unstructured and diverse, resulting in a signific…