Prediction of Runtime Parameters of Parallel Chemistry Applications via Active and Generative Learning
arXiv:2606.16226v1 Announce Type: new Abstract: 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 active and generative learning together with the empirically determined gradient boosted regression tree models chosen among a rich suite of machine learning models. When evaluated on Coupled-Cluster with Singles and Doubles computations, our models achieve a mean absolute error percentage (MAPE) as low as 0.023 and a coefficient of determination as high as 99.9%. Furtherm
The increasing complexity and computational demands of cutting-edge scientific simulations, particularly in chemistry, necessitate more efficient resource allocation and prediction methods, aligning with current advancements in AI and machine learning.
Optimizing runtime parameters for parallel chemistry applications through AI can significantly reduce computational costs and accelerate research and development in critical fields like drug discovery and materials science.
The ability to predict runtime parameters with high accuracy means that traditional trial-and-error resource allocation for complex simulations can be largely replaced by AI-driven optimization, leading to faster and more resource-efficient scientific computing.
- · High-performance computing centers
- · Pharmaceutical companies
- · Material science researchers
- · AI/ML algorithm developers
- · Traditional manual optimization methods
- · Inefficient computational chemistry workflows
Accelerated discovery of new molecules and materials due to more efficient computational chemistry.
Increased demand for specialized AI models and computational resources capable of handling scientific data and complex simulations.
Potential for AI-driven design and optimization loops becoming standard across various scientific disciplines, reducing human intervention in early-stage research.
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Read at arXiv cs.LG