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Descriptor-Free Collective Variables from Geometric Graph Neural Networks. | LitMetric

AI Article Synopsis

  • - The text discusses enhanced sampling simulations, which allow for the study of rare events in computational research by defining collective variables (CVs) that simplify complex processes.
  • - Recent advancements in machine learning have paved the way for semi-automated CV design, but most existing methods still require manual input of physical descriptors using feedforward neural networks.
  • - The authors propose a novel approach using graph neural networks to automatically derive CVs directly from atomic coordinates, ensuring invariant variables and providing tools for physical interpretation, which has been validated across various systems.

Article Abstract

Enhanced sampling simulations make the computational study of rare events feasible. A large family of such methods crucially depends on the definition of some collective variables (CVs) that could provide a low-dimensional representation of the relevant physics of the process. Recently, many methods have been proposed to semiautomatize the CV design by using machine learning tools to learn the variables directly from the simulation data. However, most methods are based on feedforward neural networks and require some user-defined physical descriptors. Here, we propose bypassing this step using a graph neural network to directly use the atomic coordinates as input for the CV model. This way, we achieve a fully automatic approach to CV determination that provides variables invariant under the relevant symmetries, especially the permutational one. Furthermore, we provide different analysis tools to favor the physical interpretation of the final CV. We prove the robustness of our approach using different methods from the literature for the optimization of the CV, and we prove its efficacy on several systems, including a small peptide, an ion dissociation in explicit solvent, and a simple chemical reaction.

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Source
http://dx.doi.org/10.1021/acs.jctc.4c01197DOI Listing

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