Data-Driven Spectral Prediction for Accelerating Large-Scale Electronic Structure Calculations
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866910273684635648 |
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| author | Badrinarayanan, Abhiram Davidovic, Davor Di Napoli, Edoardo Novak, Jurica Genovese, Luigi Ramirez-Hidalgo, Gustavo Wu, Xinzhe |
| author_facet | Badrinarayanan, Abhiram Davidovic, Davor Di Napoli, Edoardo Novak, Jurica Genovese, Luigi Ramirez-Hidalgo, Gustavo Wu, Xinzhe |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_00401 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Data-Driven Spectral Prediction for Accelerating Large-Scale Electronic Structure Calculations Badrinarayanan, Abhiram Davidovic, Davor Di Napoli, Edoardo Novak, Jurica Genovese, Luigi Ramirez-Hidalgo, Gustavo Wu, Xinzhe Computational Physics Materials Science Machine Learning Numerical Analysis 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. |
| title | Data-Driven Spectral Prediction for Accelerating Large-Scale Electronic Structure Calculations |
| topic | Computational Physics Materials Science Machine Learning Numerical Analysis |
| url | https://arxiv.org/abs/2606.00401 |