2 papers
cs.LG2026
Prediction of Runtime Parameters of Parallel Chemistry Applications via Active and Generative Learning
Tanzila Tabassum, Omer Subasi, Ajay Panyala +5
In this work, we develop two main Machine Learning based approaches to predict the runtime parameters of highly scalable parallel chemistry computations.These approaches employ act…
cs.LG2025
Guiding Application Users via Estimation of Computational Resources for Massively Parallel Chemistry Computations
Tanzila Tabassum, Omer Subasi, Ajay Panyala +6
In this work, we develop machine learning (ML) based strategies to predict resources (costs) required for massively parallel chemistry computations, such as coupled-cluster methods…