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Fostering accuracy in modelling materials and molecular complexes with quantum Monte Carlo

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Computer simulations are becoming useful in providing insight in the physical and chemical processes taking places in nature. Simulations yield molecular level understanding, which is often complementary information to the understanding provided by experimental investigations. Yet, they are only useful when they can accurately model the physical system. High accuracy is often obtained by resorting to first principles, and by modelling the quantum mechanics features of the system of interest at the atomic level. Thriving nanotechnologies and exciting experiments pose big challenges to computational approaches. On the one hand, the systems to be simulated are large and computationally expensive, and their physical and thermal properties require sampling of a large phase space (using molecular dynamics or other techniques). On the other hand, the high accuracy required to evaluate inter-atomic interactions often means using very accurate and expensive approaches to solve the Schrödinger equation. Higher accuracy can be fostered by employing diffusion quantum Monte Carlo (QMC), which is one of the most accurate approaches available to assess the ground state electronic states and their properties in molecular systems, solids and surfaces. Pairing QMC with computationally cheaper approaches and machine learning techniques allows to bring this accuracy to large systems and to investigate many complex phenomena. We see here the application to the phases of bulk ice, the phases of nano-confined water, and the study of molecular complexes. Moreover, we discuss recent methodological improvements in QMC , and the present and future challenges for QMC approaches.

This talk is part of the Lennard-Jones Centre series.

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