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The process of self-assembly of biomolecules underlies the formation of macromolecular assemblies, biomolecular materials and protein folding, and thereby is critical in many disciplines and related applications. This process typically spans numerous spatiotemporal scales and hence, is well suited for scientific interrogation via coarse-grained (CG) models used in conjunction with a suitable computational approach. This perspective provides a discussion on different coarse-graining approaches which have been used to develop CG models that resolve the process of self-assembly of biomolecules.
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Constructing coarse-grained models with physics-guided Gaussian process regression
Coarse-grained models describe the macroscopic mean response of a process at large scales, which derives from stochastic processes at small scales. Common examples include accounting for velocity fluctuations in a turbulent fluid flow model and cloud evolution in climate models. Most existing techniques for constructing coarse-grained models feature ill-defined parameters whose values are arbitrarily chosen (e.g., a window size), are narrow in their applicability (e.g., only applicable to time series or spatial data), or cannot readily incorporate physics information. Here, we introduce the concept of physics-guided Gaussian process regression as a machine-learning-based coarse-graining technique that is broadly applicable and amenable to input from known physics-based relationships. Using a pair of case studies derived from molecular dynamics simulations, we demonstrate the attractive properties and superior performance of physics-guided Gaussian processes for coarse-graining relative to prevalent benchmarks. The key advantage of Gaussian-process-based coarse-graining is its ability to seamlessly integrate data-driven and physics-based information.
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- Award ID(s):
- 2034074
- PAR ID:
- 10588125
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- APL Machine Learning
- Volume:
- 2
- Issue:
- 2
- ISSN:
- 2770-9019
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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