This workshop focuses on numerical methods for response functions and time-dependent properties, exploiting similarity of the solutions of electronic structure problems under perturbations. Electronic structure methods include conceptual DFT, derivative properties and methods (coupled-perturbed Kohn-Sham or Hartree-Fock), optical properties, excited states and conical intersections. We focus on molecules in gas phase with gradient-based learning, operator approximations, and manifold learning. We will explore dynamical systems through the lens of neuro-algebraic geometry, leveraging machine learning techniques to enhance algebraic and geometric modeling.
Copyright. All Rights Reserved.