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Found 7 papers, 4 papers with code
34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery
Large Language Models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more.
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions.
Training Neural Networks as Recognizers of Formal Languages
We provide results on a variety of languages across the Chomsky hierarchy for three neural architectures: a simple RNN, an LSTM, and a causally-masked transformer.
MatViX: Multimodal Information Extraction from Visually Rich Articles
We introduce \textsc{MatViX}, a benchmark consisting of $324$ full-length research articles and $1, 688$ complex structured JSON files, carefully curated by domain experts.
IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations
Current foundation models exhibit impressive capabilities when prompted either with text only or with both image and text inputs.
Extracting Polymer Nanocomposite Samples from Full-Length Documents
This paper investigates the use of large language models (LLMs) for extracting sample lists of polymer nanocomposites (PNCs) from full-length materials science research papers.
Approximating CKY with Transformers
We investigate the ability of transformer models to approximate the CKY algorithm, using them to directly predict a sentence's parse and thus avoid the CKY algorithm's cubic dependence on sentence length.
