Data-Driven Spectral Prediction for Accelerating Large-Scale Electronic Structure Calculations

• URL: http://arxiv.org/abs/2606.00401v1
• Date: Fri, 29 May 2026 22:35:15 GMT
• Title: Data-Driven Spectral Prediction for Accelerating Large-Scale Electronic Structure Calculations
• Abstract summary: We investigate three machine learning models trained on a novel 2 TB dataset of protein dimers.<n>The predicted spectra provide initial guesses that effectively bypass early Self-Consistent Field iterations in BigDFT.<n>These spectral predictors will be deployed to dynamically optimize upcoming rational filter-based eigensolvers, such as FrASE, which is currently in initial development.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Simulating large molecular systems comprising thousands of atoms requires highly scalable methodologies. While modern Density Functional Theory (DFT) codes exhibit linear scaling, solving the associated large, sparse generalized eigenproblems remains a critical computational bottleneck on exascale architectures. In the context of the LimitX project, we propose a data-driven framework to accelerate these calculations. By shifting the machine learning target from discrete eigenvalues to the coefficients of an interpolating Chebyshev polynomial, and by comparing both all-atom and fragment-based structural representations, we successfully overcome the dimensionality constraints of large-scale spectral prediction. We investigate three machine learning models (Kernel Ridge Regression, Graph Neural Networks, and Random Forests) trained on a novel 2 TB dataset of protein dimers. The predicted spectra provide initial guesses that effectively bypass early Self-Consistent Field (SCF) iterations in BigDFT. Ultimately, these spectral predictors will be deployed to dynamically optimize upcoming rational filter-based eigensolvers, such as FrASE, which is currently in initial development.

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