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MCQST2021 | Towards an exact quantum-mechanical description of mol. matter (Christian Ochsenfeld)
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Towards an exact quantum-mechanical description of molecular matter - challenges in solving Schrödinger's equation for complex systems
Speaker: Christian Ochsenfeld | LMU München & MCQST
Abstract
Solving the Schrödinger equation is key to the quantum-mechanical description of molecular matter in an exact non-relativistic fashion. While an analytic solution is for many-electron systems impossible, a hierarchy of approximations has been introduced over many decades to systematically converge towards the exact solution in accounting for the decisive electron-correlation effects. This hierarchy of approximations allows in principle to systematically check the reliability of computational predictions, so that quantum-chemical calculations have become an important tool for gaining deeper insights into molecular systems. However, the applicability to large systems is hampered by the strong polynomial increase of the computational effort with system size. Therefore, the central goal of our work is to overcome this scaling wall and to develop linear- and for specific properties even sublinear-scaling methods. In combination with low prefactor implementations on both CPUs and GPUs, this opens up new possibilities for computing large and complex systems.Get entangled with us!
Speaker: Christian Ochsenfeld | LMU München & MCQST
Abstract
Solving the Schrödinger equation is key to the quantum-mechanical description of molecular matter in an exact non-relativistic fashion. While an analytic solution is for many-electron systems impossible, a hierarchy of approximations has been introduced over many decades to systematically converge towards the exact solution in accounting for the decisive electron-correlation effects. This hierarchy of approximations allows in principle to systematically check the reliability of computational predictions, so that quantum-chemical calculations have become an important tool for gaining deeper insights into molecular systems. However, the applicability to large systems is hampered by the strong polynomial increase of the computational effort with system size. Therefore, the central goal of our work is to overcome this scaling wall and to develop linear- and for specific properties even sublinear-scaling methods. In combination with low prefactor implementations on both CPUs and GPUs, this opens up new possibilities for computing large and complex systems.Get entangled with us!