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UID:MEC-01161aaa0b6d1345dd8fe4e481144d84@mlqx.quantumexcellence.org
DTSTART:20210707T133000Z
DTEND:20210707T140000Z
DTSTAMP:20210610T120200Z
CREATED:20210610
LAST-MODIFIED:20210701
SUMMARY:Yahya Saleh (CFEL, DESY and Universität Hamburg, Germany)

**Active and spectral learning for the quantum dynamics of weakly-bound molecular complexes**
DESCRIPTION:Weakly-bound complexes are appealing as model systems for ultrafast-dynamics studies of bimolecular chemical reactions. Unraveling the dynamical basis of such molecular and chemical processes requires an accurate quantum mechanical description of these systems. However, this is very challenging due to the large-amplitude character of the motions, resulting in potential-energy surfaces with complicated shapes, e.g., many minima and connecting saddle points. The exponential scaling of the computational burden with the number of degrees of freedom, i.e., size of the molecular system, renders the computational costs of existing variational methods unaffordable for clusters composed of more than two moieties.\nHere, we present our recent application of machine-learning algorithms for simulating the dynamics of the weakly-bound pyrrole-water dimer. In particular, we present a new active-learning algorithm designed for the construction of multi-dimensional ab initio potential energy surfaces using a minimum number of electronic structure calculations [1]. Furthermore, we present our ongoing efforts to construct a reliable and computationally efficient nonlinear variational framework to simultaneously compute several eigenfunctions of linear Hamiltonians acting on infinite-dimensional Hilbert spaces.\n[1] Y. Saleh, V. Sanjay, A. Iske, A. Yachmenev, and J. Küpper, J. Chem. Phys., submitted (2021); arXiv:2104.00708 [physics].\n
URL:http://mlqx.quantumexcellence.org/index.php/events/jochen-kupper/
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