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All-Atom Biomolecular Simulation in the Exascale Era.

Thomas L Beck¹, Paolo Carloni^2,3, Dilipkumar N Asthagiri¹

¹National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States.

Journal of Chemical Theory and Computation

|February 21, 2024

Summary

This summary is machine-generated.

Exascale supercomputers and AI/ML enable large-scale biomolecular simulations, revolutionizing our understanding of biological processes. Future research will focus on optimizing resources and tackling grand challenge problems in atomic-level simulations.

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Area of Science:

Computational biology
Biophysics
Artificial intelligence

Background:

Exascale supercomputers are enabling dynamic simulations of biomolecular motions.
Artificial intelligence and machine learning (AI/ML) techniques are crucial for these simulations.
These advancements allow modeling over unprecedented length and time scales.

Purpose of the Study:

To report on major advances in large-scale biomolecular simulations discussed at a CECAM workshop.
To highlight current capabilities and future possibilities of exascale computing in biology.
To identify challenges and grand challenge problems for future research.

Main Methods:

Focus on atomic-level simulations.
Leveraging exascale supercomputing power.
Application of AI/ML techniques.

Main Results:

Examples of current large-scale biomolecular simulations were presented.
Future possibilities enabled by exascale computing were discussed.
Challenges in optimizing resource usage were identified.

Conclusions:

Exascale computing and AI/ML are set to revolutionize biomolecular simulations.
Overcoming resource optimization challenges is key.
Several grand challenge problems are now addressable with new computer architectures.