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This ICML 2026 paper introduces Derivative Informed XC-Loss (DI-Loss), a training approach for machine-learned exchange-correlation functionals that incorporates first and second derivative supervision on the Grassmannian of density matrices. Across four architectures, DI-Loss reduces total-energy MAE by 66% compared to energy and density supervision alone, and improves excited-state predictions in TDDFT calculations.
This paper introduces an adaptive on-the-fly multifidelity machine learning algorithm for quantum chemistry that autonomously determines training data composition across fidelities, reducing data generation costs by up to 30x compared to single-fidelity methods and up to 5x compared to standard multifidelity methods.
This paper introduces the Generative Quantum-inspired Kolmogorov-Arnold Eigensolver (GQKAE), a parameter-efficient architecture that replaces traditional neural components with Kolmogorov-Arnold modules to significantly reduce memory usage and improve convergence in quantum chemistry simulations.