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cond-mat.mtrl-sci2026
MANDALA: An E(3)-Equivariant Graph Neural Network Framework for Learning Electronic-Structure Operators with Observable Guidance
Bartosz Brzoza, Wiktoria Szopa, Zakaria Elabid +5
Electronic-structure calculations based on Kohn-Sham density functional theory remain indispensable in computational materials science and chemistry. Their computational cost, howe…
cond-mat.mtrl-sci2025
Machine Learning Time Propagators for Time-Dependent Density Functional Theory Simulations
Karan Shah, Attila Cangi
Time-dependent density functional theory (TDDFT) is a widely used method to investigate electron dynamics under external time-dependent perturbations such as laser fields. In this…
cond-mat.mtrl-sci2024
Materials Learning Algorithms (MALA): Scalable Machine Learning for Electronic Structure Calculations in Large-Scale Atomistic Simulations
Attila Cangi, Lenz Fiedler, Bartosz Brzoza +11
We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for…