58 citations · 97 across the 5 of their papers we have counts for
5 papers
Training a Scientific Reasoning Model for Chemistry
Siddharth M. Narayanan, James D. Braza, Ryan-Rhys Griffiths +6
Reasoning models are large language models that emit a long chain-of-thought before answering, providing both higher accuracy and explicit reasoning for their response. A major que…
BixBench: a Comprehensive Benchmark for LLM-based Agents in Computational Biology
Ludovico Mitchener, Jon M Laurent, Alex Andonian +6
Large Language Models (LLMs) and LLM-based agents show great promise in accelerating scientific research. Existing benchmarks for measuring this potential and guiding future develo…
Aviary: training language agents on challenging scientific tasks
Siddharth Narayanan, James D. Braza, Ryan-Rhys Griffiths +8
Solving complex real-world tasks requires cycles of actions and observations. This is particularly true in science, where tasks require many cycles of analysis, tool use, and exper…
Extracting human interpretable structure-property relationships in chemistry using XAI and large language models
Geemi P. Wellawatte, Philippe Schwaller
Explainable Artificial Intelligence (XAI) is an emerging field in AI that aims to address the opaque nature of machine learning models. Furthermore, it has been shown that XAI can…
Graph Neural Network Based Coarse-Grained Mapping Prediction
Zhiheng Li, Geemi P. Wellawatte, Maghesree Chakraborty +3
The selection of coarse-grained (CG) mapping operators is a critical step for CG molecular dynamics (MD) simulation. It is still an open question about what is optimal for this cho…