5 citations · 6 across the 5 of their papers we have counts for
6 papers
A reaction volume bias Monte Carlo trial for sampling chemisorption in confinement
Samiha Sharlin, Harold W. Hatch, Daniel W. Siderius +1
Molecular modeling of chemisorption with Monte Carlo requires the development of new trial moves to efficiently sample complex fluids such as water in Bronsted acid zeolites. Here,…
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Aritra Roy, Kevin Shen, Andrew MacBride +350
Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Yoel Zimmermann, Adib Bazgir, Zartashia Afzal +141
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 hyb…
In Context Learning and Reasoning for Symbolic Regression with Large Language Models
Samiha Sharlin, Tyler R. Josephson
Large Language Models (LLMs) are transformer-based machine learning models that have shown remarkable performance in tasks for which they were not explicitly trained. Here, we expl…
Incorporating Background Knowledge in Symbolic Regression using a Computer Algebra System
Charles Fox, Neil Tran, Nikki Nacion +2
Symbolic Regression (SR) can generate interpretable, concise expressions that fit a given dataset, allowing for more human understanding of the structure than black-box approaches.…
Formalizing Chemical Physics using the Lean Theorem Prover
Maxwell P. Bobbin, Samiha Sharlin, Parivash Feyzishendi +3
Chemical theory can be made more rigorous using the Lean theorem prover, an interactive theorem prover for complex mathematics. We formalize the Langmuir and BET theories of adsorp…