Data-Driven Spectral Prediction for Accelerating Large-Scale Electronic Structure Calculations
arXiv:2606.00401v1 Announce Type: cross 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, a
The increasing computational demands of large-scale scientific simulations, particularly in material science, are driving the need for more efficient algorithms and leveraging AI to overcome current bottlenecks in exascale computing initiatives like LimitX.
Accelerating electronic structure calculations is critical for materials discovery, drug design, and understanding complex physical phenomena, which are foundational to numerous advanced technologies and industries.
This data-driven approach shifts the computational burden from direct eigenvalue solving to predicting Chebyshev polynomial coefficients, potentially enabling faster and more scalable simulations for systems with thousands of atoms.
- · Material science research
- · Drug discovery companies
- · High-performance computing providers
- · AI/ML algorithm developers
- · Traditional DFT code developers reliant on legacy solvers
Significantly faster and more accurate simulation capabilities for complex molecular systems.
Reduced cost and time for R&D in areas like battery development, catalysts, and pharmaceuticals due to enhanced computational screening.
The development of novel materials with bespoke properties becomes more feasible, accelerating industrial innovation and potentially leading to new technological paradigms.
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Read at arXiv cs.LG