31 citations · 110 across the 10 of their papers we have counts for
10 papers
Diverse LLMs or Diverse Question Interpretations? That is the Ensembling Question
Rafael Rosales, Santiago Miret
Effectively leveraging diversity has been shown to improve performance for various machine learning models, including large language models (LLMs). However, determining the most ef…
Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry
Tsz Wai Ko, Bowen Deng, Marcel Nassar +7
Graph deep learning models, which incorporate a natural inductive bias for a collection of atoms, are of immense interest in materials science and chemistry. Here, we introduce the…
Foundational Large Language Models for Materials Research
Vaibhav Mishra, Somaditya Singh, Dhruv Ahlawat +7
Materials discovery and development are critical for addressing global challenges. Yet, the exponential growth in materials science literature comprising vast amounts of textual da…
From Text to Insight: Large Language Models for Materials Science Data Extraction
Mara Schilling-Wilhelmi, Martiño Ríos-García, Sherjeel Shabih +5
The vast majority of materials science knowledge exists in unstructured natural language, yet structured data is crucial for innovative and systematic materials design. Traditional…
Less can be more for predicting properties with large language models
Nawaf Alampara, Santiago Miret, Kevin Maik Jablonka
Predicting properties from coordinate-category data -- sets of vectors paired with categorical information -- is fundamental to computational science. In materials science, this ch…
Are large language models superhuman chemists?
Adrian Mirza, Nawaf Alampara, Sreekanth Kunchapu +32
Large language models (LLMs) have gained widespread interest due to their ability to process human language and perform tasks on which they have not been explicitly trained. Howeve…
Are LLMs Ready for Real-World Materials Discovery?
Santiago Miret, N M Anoop Krishnan
Large Language Models (LLMs) create exciting possibilities for powerful language processing tools to accelerate research in materials science. While LLMs have great potential to ac…
EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations
Vaibhav Bihani, Utkarsh Pratiush, Sajid Mannan +7
Equivariant graph neural networks force fields (EGraFFs) have shown great promise in modelling complex interactions in atomic systems by exploiting the graphs' inherent symmetries.…
ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts
Minghao Xu, Xinyu Yuan, Santiago Miret +1
Current protein language models (PLMs) learn protein representations mainly based on their sequences, thereby well capturing co-evolutionary information, but they are unable to exp…
Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model
Parishad BehnamGhader, Santiago Miret, Siva Reddy
Augmenting pretrained language models with retrievers has shown promise in effectively solving common NLP problems, such as language modeling and question answering. In this paper,…