Scientific large language models: A survey on biological & chemical domains
Large Language Models (LLMs) have emerged as a transformative power in enhancing
natural language comprehension, representing a significant stride toward artificial general …
natural language comprehension, representing a significant stride toward artificial general …
Metal–phenolic network composites: from fundamentals to applications
Composites with tailored compositions and functions have attracted widespread scientific
and industrial interest. Metal–phenolic networks (MPNs), which are composed of phenolic …
and industrial interest. Metal–phenolic networks (MPNs), which are composed of phenolic …
Structured information extraction from scientific text with large language models
Extracting structured knowledge from scientific text remains a challenging task for machine
learning models. Here, we present a simple approach to joint named entity recognition and …
learning models. Here, we present a simple approach to joint named entity recognition and …
A GPT‐4 Reticular Chemist for Guiding MOF Discovery
We present a new framework integrating the AI model GPT‐4 into the iterative process of
reticular chemistry experimentation, leveraging a cooperative workflow of interaction …
reticular chemistry experimentation, leveraging a cooperative workflow of interaction …
Sha** the water-harvesting behavior of metal–organic frameworks aided by fine-tuned GPT models
We construct a data set of metal–organic framework (MOF) linkers and employ a fine-tuned
GPT assistant to propose MOF linker designs by mutating and modifying the existing linker …
GPT assistant to propose MOF linker designs by mutating and modifying the existing linker …
Chatgpt research group for optimizing the crystallinity of mofs and cofs
We leveraged the power of ChatGPT and Bayesian optimization in the development of a
multi-AI-driven system, backed by seven large language model-based assistants and …
multi-AI-driven system, backed by seven large language model-based assistants and …
Large language models for inorganic synthesis predictions
We evaluate the effectiveness of pretrained and fine-tuned large language models (LLMs)
for predicting the synthesizability of inorganic compounds and the selection of precursors …
for predicting the synthesizability of inorganic compounds and the selection of precursors …
A review of large language models and autonomous agents in chemistry
Large language models (LLMs) have emerged as powerful tools in chemistry, significantly
impacting molecule design, property prediction, and synthesis optimization. This review …
impacting molecule design, property prediction, and synthesis optimization. This review …
[HTML][HTML] Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation
Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry
for the creation and optimization of electrocatalysts, which enhance key electrochemical …
for the creation and optimization of electrocatalysts, which enhance key electrochemical …
Cross-prediction-powered inference
While reliable data-driven decision-making hinges on high-quality labeled data, the
acquisition of quality labels often involves laborious human annotations or slow and …
acquisition of quality labels often involves laborious human annotations or slow and …