Molecular relaxation by reverse diffusion with time step prediction
Abstract
Molecular relaxation, finding the equilibrium state of a non-equilibrium structure, is an essential component of computational chemistry to understand reactivity. Classical force field (FF) methods often rely on insufficient local energy minimization, while neural network FF models require large labeled datasets encompassing both equilibrium and non-equilibrium structures. As a remedy, we propose MoreRed, molecular relaxation by reverse diffusion, a conceptually novel and purely statistical approach where non-equilibrium structures are treated as noisy instances of their corresponding equilibrium states. To enable the denoising of arbitrarily noisy inputs via a generative diffusion model, we further introduce a novel diffusion time step predictor. Notably, MoreRed learns a simpler pseudo potential energy surface (PES) instead of the complex physical PES. It is trained on a significantly smaller, and thus computationally cheaper, dataset consisting of solely unlabeled equilibrium structures, avoiding the computation of non-equilibrium structures altogether. We compare MoreRed to classical FFs, equivariant neural network FFs trained on a large dataset of equilibrium and non-equilibrium data, as well as a semi-empirical tight-binding model. To assess this quantitatively, we evaluate the root-mean-square deviation between the found equilibrium structures and the reference equilibrium structures as well as their energies.
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Research Focus: The paper presents MoreRed, a novel approach for molecular relaxation via reverse diffusion, specifically targeting the prediction of time steps to manage molecular structures transitioning from non-equilibrium to equilibrium states.
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Methodology: MoreRed utilizes generative diffusion models to treat non-equilibrium molecular structures as noisy instances of their equilibrium counterparts. It incorporates a diffusion time step predictor, allowing the model to adaptively estimate noise levels from non-equilibrium structures.
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Results: The implementation of MoreRed showed favorable performance in mapping non-equilibrium structures back to their corresponding equilibrium states, achieving low root-mean-square deviations (RMSD) and maintaining chemical accuracy with fewer training structures compared to traditional methodologies.
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Key Contributions: MoreRed enhances data efficiency by requiring only unlabeled equilibrium structures for training while introducing an adaptive time step prediction mechanism that improves the accuracy of molecular relaxation across various noise levels.
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Significance: This work addresses the computational challenges in molecular relaxation, particularly the need for large labeled datasets in classical machine learning force field models, thereby expanding the applicability of machine learning methods in computational chemistry.
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Broader Applications: MoreRed's techniques can be applied to various fields involving molecular structure optimization, including drug design, catalysis, and material science, offering a robust tool for navigating and optimizing complex chemical systems.